Release Notes¶
Version History¶
This table tracks the meta-package versions and the version of each Qiskit element installed:
Qiskit Metapackage Version |
qiskit-terra |
qiskit-aer |
qiskit-ignis |
qiskit-ibmq-provider |
qiskit-aqua |
|---|
Catatan
For the 0.7.0, 0.7.1, and 0.7.2 meta-package releases the
Versioning policy was not formalized yet.
Notable Changes¶
Qiskit 0.20.0¶
Terra 0.15.1¶
Prelude¶
The 0.15.0 release includes several new features and bug fixes. Some highlights for this release are:
This release includes the introduction of arbitrary basis translation to the transpiler. This includes support for directly targeting a broader range of device basis sets, e.g. backends implementing RZ, RY, RZ, CZ or iSwap gates.
The QuantumCircuit class now tracks global
phase. This means controlling a circuit which has global phase now
correctly adds a relative phase, and gate matrix definitions are now
exact rather than equal up to a global phase.
New Features¶
A new DAG class
qiskit.dagcircuit.DAGDependencyfor representing the dependency form of circuit, In this DAG, the nodes are operations (gates, measure, barrier, etc...) and the edges corresponds to non-commutation between two operations.Four new functions are added to
qiskit.convertersfor converting back and forth toDAGDependency. These functions are:circuit_to_dagdependency()to convert from aQuantumCircuitobject to aDAGDependencyobject.dagdependency_to_circuit()to convert from aDAGDependencyobject to aQuantumCircuitobject.dag_to_dagdependency()to convert from aDAGCircuitobject to aDAGDependencyobject.dagdependency_to_ciruit()to convert from aDAGDependencyobject to aDAGCircuitobject.
For example:
from qiskit.converters.dagdependency_to_circuit import dagdependency_to_circuit from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit circuit_in = QuantumCircuit(2) circuit_in.h(qr[0]) circuit_in.h(qr[1]) dag_dependency = circuit_to_dagdependency(circuit_in) circuit_out = dagdepency_to_circuit(dag_dependency)
Two new transpiler passes have been added to
qiskit.transpiler.passesThe first,UnrollCustomDefinitions, unrolls all instructions in the circuit according to theirdefinitionproperty, stopping when reaching either the specifiedbasis_gatesor a set of gates in the providedEquivalenceLibrary. The second,BasisTranslator, uses the set of translations in the providedEquivalenceLibraryto re-write circuit instructions in a specified basis.A new
translation_methodkeyword argument has been added totranspile()to allow selection of the method to be used for translating circuits to the available device gates. For example,transpile(circ, backend, translation_method='translator'). Valid choices are:'unroller': to use theUnrollerpass'translator': to use theBasisTranslatorpass.'synthesis': to use theUnitarySynthesispass.
The default value is
'translator'.A new class for handling counts result data,
qiskit.result.Counts, has been added. This class is a subclass ofdictand can be interacted with like any other dictionary. But, it includes helper methods and attributes for dealing with counts results from experiments and also handles post processing and formatting of binary strings at object initialization. ACountsobject can be created by passing a dictionary of counts with the keys being either integers, hexadecimal strings of the form'0x4a', binary strings of the form'0b1101', a bit string formatted across register and memory slots (ie'00 10'), or a dit string. For example:from qiskit.result import Counts counts = Counts({"0x0': 1, '0x1', 3, '0x2': 1020})
A new method for constructing
qiskit.dagcircuit.DAGCircuitobjects has been added,from_networkx(). This method takes in a networkxMultiDiGraphobject (in the format returned byto_networkx()) and will return a newDAGCircuitobject. The intent behind this function is to enable transpiler pass authors to leverage networkx's graph algorithm library if a function is missing from the retworkx API. Although, hopefully in such casses an issue will be opened with retworkx issue tracker (or even better a pull request submitted).A new kwarg for
init_qubitshas been added toassemble()andexecute(). For backends that support this featureinit_qubitscan be used to control whether the backend executing the circuits inserts any initialization sequences at the start of each shot. By default this is set toTruemeaning that all qubits can assumed to be in the ground state at the start of each shot. However, wheninit_qubitsis set toFalsequbits will be uninitialized at the start of each experiment and between shots. Note, that the backend running the circuits has to support this feature for this flag to have any effect.A new kwarg
rep_delayhas been added toqiskit.compiler.assemble(),qiskit.execute.execute(), and the constructor forPulseQobjtConfig.qiskit This new kwarg is used to denotes the time between program executions. It must be chosen from the list of valid values set as therep_delaysfrom a backend'sPulseBackendConfigurationobject which can be accessed asbackend.configuration().rep_delays).The
rep_delaykwarg will only work on backends which allow for dynamic repetition time. This will also be indicated in thePulseBackendConfigurationobject for a backend as thedynamic_reprate_enabledattribute. Ifdynamic_reprate_enabledisFalsethen therep_timevalue specified forqiskit.compiler.assemble(),qiskit.execute.execute(), or the constructor forPulseQobjtConfigwill be used rather thanrep_delay.rep_timeonly allows users to specify the duration of a program, rather than the delay between programs.The
qobj_schema.jsonJSON Schema file inqiskit.schemashas been updated to include therep_delayas an optional configuration property for pulse qobjs.The
backend_configuration_schema.jsonJSON Schema file in mod:qiskit.schemas has been updated to includerep_delay_rangeanddefault_rep_delayas optional properties for a pulse backend configuration.A new attribute,
global_phase, which is is used for tracking the global phase has been added to theqiskit.circuit.QuantumCircuitclass. For example:import math from qiskit import QuantumCircuit circ = QuantumCircuit(1, global_phase=math.pi) circ.u1(0)
The global phase may also be changed or queried with
circ.global_phasein the above example. In either case the setting is in radians. If the circuit is converted to an instruction or gate the global phase is represented by two single qubit rotations on the first qubit.This allows for other methods and functions which consume a
QuantumCircuitobject to take global phase into account. For example. with theglobal_phaseattribute theto_matrix()method for a gate can now exactly correspond to its decompositions instead of just up to a global phase.The same attribute has also been added to the
DAGCircuitclass so that global phase can be tracked when converting betweenQuantumCircuitandDAGCircuit.Two new classes,
AncillaRegisterandAncillaQubithave been added to theqiskit.circuitmodule. These are subclasses ofQuantumRegisterandQubitrespectively and enable marking qubits being ancillas. This will allow these qubits to be re-used in larger circuits and algorithms.A new method,
control(), has been added to theQuantumCircuit. This method will return a controlled version of theQuantumCircuitobject, with both open and closed controls. This functionality had previously only been accessible via theGateclass.A new method
repeat()has been added to theQuantumCircuitclass. It returns a new circuit object containing a specified number of repetitions of the original circuit. For example:from qiskit.circuit import QuantumCircuit qc = QuantumCircuit(2) qc.h(0) qc.cx(0, 1) repeated_qc = qc.repeat(3) repeated_qc.decompose().draw(output='mpl')
The parameters are copied by reference, meaning that if you update the parameters in one instance of the circuit all repetitions will be updated.
A new method
reverse_bits()has been added to theQuantumCircuitclass. This method will reverse the order of bits in a circuit (both quantum and classical bits). This can be used to switch a circuit from little-endian to big-endian and vice-versa.A new method,
combine_into_edge_map(), was added to theqiskit.transpiler.Layoutclass. This method enables converting converting twoLayoutobjects into a qubit map for composing two circuits.A new class,
ConfigurableFakeBackend, has been added to theqiskit.test.mock.utilsmodule. This new class enables the creation of configurable mock backends for use in testing. For example:from qiskit.test.mock.utils import ConfigurableFakeBackend backend = ConfigurableFakeBackend("Tashkent", n_qubits=100, version="0.0.1", basis_gates=['u1'], qubit_t1=99., qubit_t2=146., qubit_frequency=5., qubit_readout_error=0.01, single_qubit_gates=['u1'])
will create a backend object with 100 qubits and all the other parameters specified in the constructor.
A new method
draw()has been added to theqiskit.circuit.EquivalenceLibraryclass. This method can be used for drawing the contents of an equivalence library, which can be useful for debugging. For example:from numpy import pi from qiskit.circuit import EquivalenceLibrary from qiskit.circuit import QuantumCircuit from qiskit.circuit import QuantumRegister from qiskit.circuit import Parameter from qiskit.circuit.library import HGate from qiskit.circuit.library import U2Gate from qiskit.circuit.library import U3Gate my_equiv_library = EquivalenceLibrary() q = QuantumRegister(1, 'q') def_h = QuantumCircuit(q) def_h.append(U2Gate(0, pi), [q[0]], []) my_equiv_library.add_equivalence(HGate(), def_h) theta = Parameter('theta') phi = Parameter('phi') lam = Parameter('lam') def_u2 = QuantumCircuit(q) def_u2.append(U3Gate(pi / 2, phi, lam), [q[0]], []) my_equiv_library.add_equivalence(U2Gate(phi, lam), def_u2) my_equiv_library.draw()
A new Phase instruction,
SetPhase, has been added toqiskit.pulse. This instruction sets the phase of the subsequent pulses to the specified phase (in radians. For example:import numpy as np from qiskit.pulse import DriveChannel from qiskit.pulse import Schedule from qiskit.pulse import SetPhase sched = Schedule() sched += SetPhase(np.pi, DriveChannel(0))
In this example, the phase of the pulses applied to
DriveChannel(0)after theSetPhaseinstruction will be set to \(\pi\) radians.A new pulse instruction
ShiftFrequencyhas been added toqiskit.pulse.instructions. This instruction enables shifting the frequency of a channel from its set frequency. For example:from qiskit.pulse import DriveChannel from qiskit.pulse import Schedule from qiskit.pulse import ShiftFrequency sched = Schedule() sched += ShiftFrequency(-340e6, DriveChannel(0))
In this example all the pulses applied to
DriveChannel(0)after theShiftFrequencycommand will have the envelope a frequency decremented by 340MHz.A new method
conjugate()has been added to theParameterExpressionclass. This enables callingnumpy.conj()without raising an error. Since aParameterExpressionobject is real, it will return itself. This behaviour is analogous to Python floats/ints.A new class
PhaseEstimationhas been added toqiskit.circuit.library. This circuit library class is the circuit used in the original formulation of the phase estimation algorithm in arXiv:quant-ph/9511026. Phase estimation is the task to to estimate the phase \(\phi\) of an eigenvalue \(e^{2\pi i\phi}\) of a unitary operator \(U\), provided with the corresponding eigenstate \(|psi\rangle\). That is\[U|\psi\rangle = e^{2\pi i\phi} |\psi\rangle\]This estimation (and thereby this circuit) is a central routine to several well-known algorithms, such as Shor's algorithm or Quantum Amplitude Estimation.
The
qiskit.visualizationfunctionplot_state_qsphere()has a new kwargshow_state_labelswhich is used to control whether each blob in the qsphere visualization is labeled. By default this kwarg is set toTrueand shows the basis states next to each blob by default. This feature can be disabled, reverting to the previous behavior, by setting theshow_state_labelskwarg toFalse.The
qiskit.visualizationfunctionplot_state_qsphere()has a new kwargshow_state_phaseswhich is set toFalseby default. When set toTrueit displays the phase of each basis state.The
qiskit.visualizationfunctionplot_state_qsphere()has a new kwarguse_degreeswhich is set toFalseby default. When set toTrueit displays the phase of each basis state in degrees, along with the phase circle at the bottom right.A new class,
QuadraticFormto theqiskit.circuit.librarymodule for implementing a a quadratic form on binary variables. The circuit library element implements the operation\[|x\rangle |0\rangle \mapsto |x\rangle |Q(x) \mod 2^m\rangle\]for the quadratic form \(Q\) and \(m\) output qubits. The result is in the \(m\) output qubits is encoded in two's complement. If \(m\) is not specified, the circuit will choose the minimal number of qubits required to represent the result without applying a modulo operation. The quadratic form is specified using a matrix for the quadratic terms, a vector for the linear terms and a constant offset. If all terms are integers, the circuit implements the quadratic form exactly, otherwise it is only an approximation.
For example:
import numpy as np from qiskit.circuit.library import QuadraticForm A = np.array([[1, 2], [-1, 0]]) b = np.array([3, -3]) c = -2 m = 4 quad_form_circuit = QuadraticForm(m, A, b, c)
Add
qiskit.quantum_info.Statevector.expectation_value()andqiskit.quantum_info.DensityMatrix.expectation_value()methods for computing the expectation value of anqiskit.quantum_info.Operator.For the
seedkwarg in the constructor forqiskit.circuit.library.QuantumVolumenumpy random Generator objects can now be used. Previously, only integers were a valid input. This is useful when integratingQuantumVolumeas part of a larger function with its own random number generation, e.g. generating a sequence ofQuantumVolumecircuits.The
QuantumCircuitmethodcompose()has a new kwargfrontwhich can be used for prepending the other circuit before the origin circuit instead of appending. For example:from qiskit.circuit import QuantumCircuit circ1 = QuantumCircuit(2) circ2 = QuantumCircuit(2) circ2.h(0) circ1.cx(0, 1) circ1.compose(circ2, front=True).draw(output='mpl')
Two new passes,
SabreLayoutandSabreSwapfor layout and routing have been added toqiskit.transpiler.passes. These new passes are based on the algorithm presented in Li et al., "Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices", ASPLOS 2019. They can also be selected when using thetranspile()function by setting thelayout_methodkwarg to'sabre'and/or therouting_methodto'sabre'to useSabreLayoutandSabreSwaprespectively.Added the method
replace()to theqiskit.pulse.Scheduleclass which allows a pulse instruction to be replaced with another. For example:.. code-block:: python
from qiskit import pulse
d0 = pulse.DriveChannel(0)
sched = pulse.Schedule()
old = pulse.Play(pulse.Constant(100, 1.0), d0) new = pulse.Play(pulse.Constant(100, 0.1), d0)
sched += old
sched = sched.replace(old, new)
assert sched == pulse.Schedule(new)
Added new gate classes to
qiskit.circuit.libraryfor the \(\sqrt{X}\), its adjoint \(\sqrt{X}^\dagger\), and controlled \(\sqrt{X}\) gates asSXGate,SXdgGate, andCSXGate. They can also be added to aQuantumCircuitobject using thesx(),sxdg(), andcsx()respectively.Add support for
Resetinstructions toqiskit.quantum_info.Statevector.from_instruction(). Note that this involves RNG sampling in choosing the projection to the zero state in the case where the qubit is in a superposition state. The seed for sampling can be set using theseed()method.The methods
qiskit.circuit.ParameterExpression.subs()andqiskit.circuit.QuantumCircuit.assign_parameters()now acceptParameterExpressionas the target value to be substituted.For example,
from qiskit.circuit import QuantumCircuit, Parameter p = Parameter('p') source = QuantumCircuit(1) source.rz(p, 0) x = Parameter('x') source.assign_parameters({p: x*x})
┌──────────┐ q_0: ┤ Rz(x**2) ├ └──────────┘The
QuantumCircuit()methodto_gate()has a new kwarglabelwhich can be used to set a label for for the outputGateobject. For example:from qiskit.circuit import QuantumCircuit circuit_gate = QuantumCircuit(2) circuit_gate.h(0) circuit_gate.cx(0, 1) custom_gate = circuit_gate.to_gate(label='My Special Bell') new_circ = QuantumCircuit(2) new_circ.append(custom_gate, [0, 1], []) new_circ.draw(output='mpl')
Added the
UGate,CUGate,PhaseGate, andCPhaseGatewith the correspondingQuantumCircuitmethodsu(),cu(),p(), andcp(). TheUGategate is the generic single qubit rotation gate with 3 Euler angles and theCUGategate its controlled version.CUGatehas 4 parameters to account for a possible global phase of the U gate. ThePhaseGateandCPhaseGategates are the general Phase gate at an arbitrary angle and it's controlled version.A new kwarg,
cregbundlehas been added to theqiskit.visualization.circuit_drawer()function and theQuantumCircuitmethoddraw(). When set toTruethe cregs will be bundled into a single line in circuit visualizations for thetextandmpldrawers. The default value isTrue. Addresses issue #4290.For example:
from qiskit import QuantumCircuit circuit = QuantumCircuit(2) circuit.measure_all() circuit.draw(output='mpl', cregbundle=True)
A new kwarg,
initial_statehas been added to theqiskit.visualization.circuit_drawer()function and theQuantumCircuitmethoddraw(). When set toTruethe initial state will now be included in circuit visualizations for all drawers. Addresses issue #4293.For example:
from qiskit import QuantumCircuit circuit = QuantumCircuit(2) circuit.measure_all() circuit.draw(output='mpl', initial_state=True)
Labels will now be displayed when using the 'mpl' drawer. There are 2 types of labels - gate labels and control labels. Gate labels will replace the gate name in the display. Control labels will display above or below the controls for a gate. Fixes issues #3766, #4580 Addresses issues #3766 and #4580.
For example:
from qiskit import QuantumCircuit from qiskit.circuit.library.standard_gates import YGate circuit = QuantumCircuit(2) circuit.append(YGate(label='A Y Gate').control(label='Y Control'), [0, 1]) circuit.draw(output='mpl')
Upgrade Notes¶
Implementations of the multi-controlled X Gate (
MCXGrayCode,MCXRecursive, andMCXVChain) have had theirnameproperties changed to more accurately describe their implementation:mcx_gray,mcx_recursive, andmcx_vchainrespectively. Previously, these gates shared the namemcxwithMCXGate, which caused these gates to be incorrectly transpiled and simulated.By default the preset passmanagers in
qiskit.transpiler.preset_passmanagersare usingUnrollCustomDefinitionsandBasisTranslatorto handle basis changing instead of the previous defaultUnroller. This was done because the new passes are more flexible and allow targeting any basis set, however the output may differ. To use the previous default you can set thetranslation_methodkwarg ontranspile()to'unroller'.The
qiskit.converters.circuit_to_gate()and :func`qiskit.converters.circuit_to_instruction` converter functions had previously automatically included the generated gate or instruction in the activeSessionEquivalenceLibrary. These converters now accept an optionalequivalence_librarykeyword argument to specify if and where the converted instances should be registered. The default behavior has changed to not register the converted instance.The default value of the
cregbundlekwarg for theqiskit.circuit.QuantumCircuit.draw()method andqiskit.visualization.circuit_drawer()function has been changed toTrue. This means that by default the classical bits in the circuit diagram will now be bundled by default, for example:from qiskit.circuit import QuantumCircuit circ = QuantumCircuit(4) circ.x(0) circ.h(1) circ.measure_all() circ.draw(output='mpl')
If you want to have your circuit drawing retain the previous behavior and show each classical bit in the diagram you can set the
cregbundlekwarg toFalse. For example:from qiskit.circuit import QuantumCircuit circ = QuantumCircuit(4) circ.x(0) circ.h(1) circ.measure_all() circ.draw(output='mpl', cregbundle=False)
Scheduleplotting withqiskit.pulse.Schedule.draw()andqiskit.visualization.pulse_drawer()will no longer display the event table by default. This can be reenabled by setting thetablekwarg toTrue.The pass
RemoveResetInZeroStatewas previously included in the preset pass managerlevel_0_pass_manager()which was used with theoptimization_level=0fortranspile()andexecute()functions. However,RemoveResetInZeroStateis an optimization pass and should not have been included in optimization level 0 and was removed. If you need to runtranspile()withRemoveResetInZeroStateeither use a custom pass manager oroptimization_level1, 2, or 3.The deprecated kwarg
line_lengthfor theqiskit.visualization.circuit_drawer()function andqiskit.circuit.QuantumCircuit.draw()method has been removed. It had been deprecated since the 0.10.0 release. Instead you can use thefoldkwarg to adjust the width of the circuit diagram.The
'mpl'output mode for theqiskit.circuit.QuantumCircuit.draw()method andcircuit_drawer()now requires the pylatexenc library to be installed. This was already an optional dependency for visualization, but was only required for the'latex'output mode before. It is now also required for the matplotlib drawer because it is needed to handle correctly sizing gates with matplotlib's mathtext labels for gates.The deprecated
get_tokensmethods for theqiskit.qasm.Qasmandqiskit.qasm.QasmParserhas been removed. These methods have been deprecated since the 0.9.0 release. Theqiskit.qasm.Qasm.generate_tokens()andqiskit.qasm.QasmParser.generate_tokens()methods should be used instead.The deprecated kwarg
channels_to_plotforqiskit.pulse.Schedule.draw(),qiskit.pulse.Instruction.draw(),qiskit.visualization.pulse.matplotlib.ScheduleDrawer.drawandpulse_drawer()has been removed. The kwarg has been deprecated since the 0.11.0 release and was replaced by thechannelskwarg, which functions identically and should be used instead.The deprecated
circuit_instruction_mapattribute of theqiskit.providers.models.PulseDefaultsclass has been removed. This attribute has been deprecated since the 0.12.0 release and was replaced by theinstruction_schedule_mapattribute which can be used instead.The
unionmethod ofScheduleandInstructionhave been deprecated since the 0.12.0 release and have now been removed. Useqiskit.pulse.Schedule.insert()andqiskit.pulse.Instruction.meth()methods instead with the kwarg``time=0``.The deprecated
scalingargument to thedrawmethod ofScheduleandInstructionhas been replaced withscalesince the 0.12.0 release and now has been removed. Use thescalekwarg instead.The deprecated
periodargument toqiskit.pulse.libraryfunctions have been replaced byfreqsince the 0.13.0 release and now removed. Use thefreqkwarg instead ofperiod.The
qiskit.pulse.commandsmodule containingCommandsclasses was deprecated in the 0.13.0 release and has now been removed. You will have to upgrade your Pulse code if you were still using commands. For example:Old
New
Command(args)(channel)Instruction(args, channel)Acquire(duration)(AcquireChannel(0))
Acquire(duration, AcquireChannel(0))
Delay(duration)(channel)
Delay(duration, channel)
FrameChange(angle)(DriveChannel(0))
# FrameChange was also renamed ShiftPhase(angle, DriveChannel(0))
Gaussian(...)(DriveChannel(0))
# Pulses need to be `Play`d Play(Gaussian(...), DriveChannel(0))
All classes and function in the
qiskit.tool.qimodule were deprecated in the 0.12.0 release and have now been removed. Instead use theqiskit.quantum_infomodule and the new methods and classes that it has for working with quantum states and operators.The
qiskit.quantum_info.basis_stateandqiskit.quantum_info.projectorfunctions are deprecated as of Qiskit Terra 0.12.0 as are now removed. Use theqiskit.quantum_info.QuantumStateand its derivativesqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixto work with states.The interactive plotting functions from
qiskit.visualization,iplot_bloch_multivector,iplot_state_city,iplot_state_qsphere,iplot_state_hinton,iplot_histogram,iplot_state_paulivecnow are just deprecated aliases for the matplotlib based equivalents and are no longer interactive. The hosted static JS code that these functions relied on has been removed and they no longer could work. A normal deprecation wasn't possible because the site they depended on no longer exists.The validation components using marshmallow from
qiskit.validationhave been removed from terra. Since they are no longer used to build any objects in terra.The marshmallow schema classes in
qiskit.resulthave been removed since they are no longer used by theqiskit.result.Resultclass.The output of the
to_dict()method for theqiskit.result.Resultclass is no longer in a format for direct JSON serialization. Depending on the content contained in instances of these classes there may be types that the default JSON encoder doesn't know how to handle, for example complex numbers or numpy arrays. If you're JSON serializing the output of theto_dict()method directly you should ensure that your JSON encoder can handle these types.The option to acquire multiple qubits at once was deprecated in the 0.12.0 release and is now removed. Specifically, the init args
mem_slotsandreg_slotshave been removed fromqiskit.pulse.instructions.Acquire, andchannel,mem_slotandreg_slotwill raise an error if a list is provided as input.Support for the use of the
USE_RETWORKXenvironment variable which was introduced in the 0.13.0 release to provide an optional fallback to the legacy networkx basedqiskit.dagcircuit.DAGCircuitimplementation has been removed. This flag was only intended as provide a relief valve for any users that encountered a problem with the new implementation for one release during the transition to retworkx.The module within
qiskit.pulseresponsible for schedule->schedule transformations has been renamed fromreschedule.pytotransforms.py. The previous import path has been deprecated. To upgrade your code:from qiskit.pulse.rescheduler import <X>
should be replaced by:
from qiskit.pulse.transforms import <X>
In previous releases a
PassManagerdid not allowTransformationPassclasses to modify thePropertySet. This restriction has been lifted so aTransformationPassclass now has read and write access to both thePropertySetandDAGCircuitduringrun(). This change was made to more efficiently facilitateTransformationPassclasses that have an internal state which may be necessary for later passes in thePassManager. Without this change a second redundantAnalysisPasswould have been necessary to recreate the internal state, which could add significant overhead.
Deprecation Notes¶
The name of the first positional parameter for the
qiskit.visualizationfunctionsplot_state_hinton(),plot_bloch_multivector(),plot_state_city(),plot_state_paulivec(), andplot_state_qsphere()has been renamed fromrhotostate. Passing in the value by name torhois deprecated and will be removed in a future release. Instead you should either pass the argument positionally or use the new parameter namestate.The
qiskit.pulse.pulse_libmodule has been deprecated and will be removed in a future release. It has been renamed toqiskit.pulse.librarywhich should be used instead.The
qiskit.circuit.QuantumCircuitmethodmirror()has been deprecated and will be removed in a future release. The methodqiskit.circuit.QuantumCircuit.reverse_ops()should be used instead, since mirroring could be confused with swapping the output qubits of the circuit. Thereverse_ops()method only reverses the order of gates that are applied instead of mirroring.The
qubits()andclbits()methods ofqiskit.dagcircuit.DAGCircuithave been deprecated and will be removed in a future release. They have been replaced with properties of the same name,qiskit.dagcircuit.DAGCircuit.qubitsandqiskit.dagcircuit.DAGCircuit.clbits, and are cached so accessing them is much faster.The
get_sample_pulsemethod forqiskit.pulse.library.ParametricPulsederived classes (for exampleGaussianSquare) has been deprecated and will be removed in a future release. It has been replaced by theget_waveformmethod (for exampleget_waveform()) which should behave identically.The use of the optional
conditionargument onqiskit.dagcircuit.DAGNode,qiskit.dagcircuit.DAGCircuit.apply_operation_back(), andqiskit.dagcircuit.DAGCircuit.apply_operation_front()has been deprecated and will be removed in a future release. Instead thecontrolset inqiskit.circuit.Instructioninstances being added to aDAGCircuitshould be used.The
set_atolandset_rtolclass methods of theqiskit.quantum_info.BaseOperatorandqiskit.quantum_info.QuantumStateclasses (and their subclasses such asOperatorandqiskit.quantum_info.DensityMatrix) are deprecated and will be removed in a future release. Instead the value for the attributes.atoland.rtolshould be set on the class instead. For example:from qiskit.quantum_info import ScalarOp ScalarOp.atol = 3e-5 op = ScalarOp(2)
The interactive plotting functions from
qiskit.visualization,iplot_bloch_multivector,iplot_state_city,iplot_state_qsphere,iplot_state_hinton,iplot_histogram,iplot_state_paulivechave been deprecated and will be removed in a future release. The matplotlib based equivalent functions fromqiskit.visualization,plot_bloch_multivector(),plot_state_city(),plot_state_qsphere(),plot_state_hinton(),plot_state_histogram(), andplot_state_paulivec()should be used instead.The properties
acquires,mem_slots, andreg_slotsof theqiskit.pulse.instructions.Acquirepulse instruction have been deprecated and will be removed in a future release. They are just duplicates ofchannel,mem_slot, andreg_slotrespectively now that previously deprecated support for using multiple qubits in a singleAcquireinstruction has been removed.The
SamplePulseclass fromqiskit.pulsehas been renamed toWaveform.SamplePulseis deprecated and will be removed in a future release.The style dictionary key
cregbundlehas been deprecated and will be removed in a future release. This has been replaced by the kwargcregbundleadded to theqiskit.visualization.circuit_drawer()function and theQuantumCircuitmethoddraw().
Bug Fixes¶
The
qiskit.circuit.QuantumCircuitmethodnum_nonlocal_gatespreviously included multi-qubitqiskit.circuit.Instructionobjects (for example,Barrier) in its count of non-local gates. This has been corrected so that only non-localGateobjects are counted. Fixes #4500ControlledGateinstances with a setctrl_statewere in some cases not being evaluated as equal, even if the compared gates were equivalent. This has been resolved so that Fixes #4573When accessing a bit from a
qiskit.circuit.QuantumRegisterorqiskit.circuit.ClassicalRegisterby index when using numpy integer types <https://numpy.org/doc/stable/user/basics.types.html>`__ would previously raise aCircuitErrorexception. This has been resolved so numpy types can be used in addition to Python's built-ininttype. Fixes #3929.A bug was fixed where only the first
qiskit.pulse.configuration.Kernelorqiskit.pulse.configuration.Discriminatorfor anqiskit.pulse.Acquirewas used when there were multiple Acquires at the same time in aqiskit.pulse.Schedule.The SI unit use for constructing
qiskit.pulse.SetFrequencyobjects is in Hz, but when aPulseQobjInstructionobject is created from aSetFrequencyinstance it needs to be converted to GHz. This conversion was missing from previous releases and has been fixed.Previously it was possible to set the number of control qubits to zero in which case the the original, potentially non-controlled, operation would be returned. This could cause an
AttributeErrorto be raised if the caller attempted to access an attribute which onlyControlledGateobject have. This has been fixed by adding a getter and setter fornum_ctrl_qubitsto validate that a valid value is being used. Fixes #4576Open controls were implemented by modifying a
Gateobjectsdefinition. However, when the gate already exists in the basis set, this definition was not used, which resulted in incorrect circuits being sent to a backend after transpilation. This has been fixed by modifying theUnrollerpass to use the definition if it encounters a controlled gate with open controls. Fixes #4437The
insert_barrierskeyword argument in theZZFeatureMapclass didn't actually insert barriers in between the Hadamard layers and evolution layers. This has been fixed so that barriers are now properly inserted.Fixed issue where some gates with three or more qubits would fail to compile in certain instances. Refer to #4577 <https://github.com/Qiskit/qiskit-terra/issues/4577 for more detail.
The matplotlib (
'mpl') output backend for theqiskit.circuit.QuantumCircuitmethoddraw()and theqiskit.visualization.circuit_drawer()function was not properly scaling when the kwargscalewas set. Fonts and line widths did not scale with the rest of the image. This has been fixed and all elements of the circuit diagram now scale properly. For example:from qiskit import QuantumCircuit circuit = QuantumCircuit(2) circuit.h(0) circuit.cx(0, 1) circuit.draw(output='mpl', scale=0.5)
Fixes #4179.
Fixes issue where initializing or evolving
qiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixclasses by circuits by circuit containingBarrierinstructions would raise an exception. Fixes #4461Previously when a
QuantumCircuitcontained aGatewith a classical condition the transpiler would sometimes fail when usingoptimization_level=3ontranspile()orexecute()raising anUnboundLocalError. This has been fixed by updating theConsolidateBlockspass to account for the classical condition. Fixes #4672.In some situations long gate and register names would overflow, or leave excessive empty space around them when using the
'mpl'output backend for theqiskit.circuit.QuantumCircuit.draw()method andqiskit.visualization.circuit_drawer()function. This has been fixed by using correct text widths for a proportional font. Fixes #4611, #4605, #4545, #4497, #4449, and #3641.When using the
style` kwarg on the :meth:`qiskit.circuit.QuantumCircuit.draw` or :func:`qiskit.visualization.circuit_drawer` with the ``'mpl'output backend the dictionary key'showindex'set toTrue, the index numbers at the top of the column did not line up properly. This has been fixed.When using
cregbunde=Truewith the'mpl'output backend for theqiskit.circuit.QuantumCircuit.draw()method andqiskit.visualization.circuit_drawer()function and measuring onto a second fold, the measure arrow would overwrite the creg count. The count was moved to the left to prevent this. Fixes #4148.When using the
'mpl'output backend for theqiskit.circuit.QuantumCircuit.draw()method andqiskit.visualization.circuit_drawer()functionCSwapGategates and a controlledRZZGategates now display with their appropriate symbols instead of in a box.When using the
'mpl'output backend for theqiskit.circuit.QuantumCircuit.draw()method andqiskit.visualization.circuit_drawer()function controlled gates created using theto_gate()method were not properly spaced and could overlap with other gates in the circuit diagram. This issue has been fixed.When using the
'mpl'output backend for theqiskit.circuit.QuantumCircuit.draw()method andqiskit.visualization.circuit_drawer()function gates with arrays as parameters, such asHamiltonianGate, no longer display with excessive space around them. Fixes #4352.When using the
'mpl'output backend for theqiskit.circuit.QuantumCircuit.draw()method andqiskit.visualization.circuit_drawer()function generic gates created by directly instantiatingqiskit.circuit.Gatemethod now display the proper background color for the gate. Fixes #4496.When using the
'mpl'output backend for theqiskit.circuit.QuantumCircuit.draw()method andqiskit.visualization.circuit_drawer()function anAttributeErrorthat occurred when usingIsometryorInitializehas been fixed. Fixes #4439.When using the
'mpl'output backend for theqiskit.circuit.QuantumCircuit.draw()method andqiskit.visualization.circuit_drawer()function some open-controlled gates did not properly display the open controls. This has been corrected so that open controls are properly displayed as open circles. Fixes #4248.When using the
'mpl'output backend for theqiskit.circuit.QuantumCircuit.draw()method andqiskit.visualization.circuit_drawer()function setting thefoldkwarg to -1 will now properly display the circuit without folding. Fixes #4506.Parametric pulses from
qiskit.pulse.library.discretenow have zero ends of parametric pulses by default. The endpoints are defined such that for a function \(f(x)\) then \(f(-1) = f(duration + 1) = 0\). Fixes #4317
Other Notes¶
The
qiskit.result.Resultclass which was previously constructed using the marshmallow library has been refactored to not depend on marshmallow anymore. This new implementation should be a seamless transition but some specific behavior that was previously inherited from marshmallow may not work. Please file issues for any incompatibilities found.
Aer 0.6.1¶
Prelude¶
This 0.6.0 release includes numerous performance improvements for all simulators in the Aer provider and significant changes to the build system when building from source. The main changes are support for SIMD vectorization, approximation in the matrix product state method via bond-dimension truncation, more efficient Pauli expectation value computation, and greatly improved efficiency in Python conversion of C++ result objects. The build system was upgraded to use the Conan to manage common C++ dependencies when building from source.
New Features¶
Add density matrix snapshot support to "statevector" and "statevector_gpu" methods of the QasmSimulator.
Allow density matrix snapshots on specific qubits, not just all qubits. This computes the partial trace of the state over the remaining qubits.
Adds Pauli expectation value snapshot support to the "density_matrix" simulation method of the
qiskit.providers.aer.QasmSimulator. Add snapshots to circuits using theqiskit.providers.aer.extensions.SnapshotExpectationValueextension.Greatly improves performance of the Pauli expectation value snapshot algorithm for the "statevector", "statevector_gpu, "density_matrix", and "density_matrix_gpu" simulation methods of the
qiskit.providers.aer.QasmSimulator.Enable the gate-fusion circuit optimization from the
qiskit.providers.aer.QasmSimulatorin both theqiskit.providers.aer.StatevectorSimulatorandqiskit.providers.aer.UnitarySimulatorbackends.Improve the performance of average snapshot data in simulator results. This effects probability, Pauli expectation value, and density matrix snapshots using the following extensions:
Add move constructor and improve memory usage of the C++ matrix class to minimize copies of matrices when moving output of simulators into results.
Improve performance of unitary simulator.
Add approximation to the "matrix_product_state" simulation method of the
QasmSimulatorto limit the bond-dimension of the MPS.There are two modes of approximation. Both discard the smallest Schmidt coefficients following the SVD algorithm. There are two parameters that control the degree of approximation:
"matrix_product_state_max_bond_dimension"(int): Sets a limit on the number of Schmidt coefficients retained at the end of the svd algorithm. Coefficients beyond this limit will be discarded. (Default: None, i.e., no limit on the bond dimension)."matrix_product_state_truncation_threshold"(double): Discard the smallest coefficients for which the sum of their squares is smaller than this threshold. (Default: 1e-16).Improve the performance of measure sampling when using the "matrix_product_state"
QasmSimulatorsimulation method.Add support for
Delay,PhaseandSetPhasepulse instructions to theqiskit.providers.aer.PulseSimulator.Improve the performance of the
qiskit.providers.aer.PulseSimulatorby caching calls to RHS functionIntroduce alternate DE solving methods, specifiable through
backend_optionsin theqiskit.providers.aer.PulseSimulator.Improve performance of simulator result classes by using move semantics and removing unnecessary copies that were happening when combining results from separate experiments into the final result object.
Greatly improve performance of pybind11 conversion of simulator results by using move semantics where possible, and by moving vector and matrix results to Numpy arrays without copies.
Change the RNG engine for simulators from 32-bit Mersenne twister to 64-bit Mersenne twister engine.
Improves the performance of the "statevector" simulation method of the
qiskit.providers.aer.QasmSimulatorandqiskit.providers.aer.StatevectorSimulatorby using SIMD intrinsics on systems that support the AVX2 instruction set. AVX2 support is automatically detected and enabled at runtime.
Upgrade Notes¶
Changes the build system to use the Conan package manager. This tool will handle most of the dependencies needed by the C++ source code. Internet connection may be needed for the first build or when dependencies are added or updated, in order to download the required packages if they are not in your Conan local repository.
When building the standalone version of qiskit-aer you must install conan first with:
pip install conan
Changes how transpilation passes are handled in the C++ Controller classes so that each pass must be explicitly called. This allows for greater customization on when each pass should be called, and with what parameters. In particular this enables setting different parameters for the gate fusion optimization pass depending on the QasmController simulation method.
Add
gate_length_unitskwarg toqiskit.providers.aer.noise.NoiseModel.from_device()for specifying customgate_lengthsin the device noise model function to handle unit conversions for internal code.Add Controlled-Y ("cy") gate to the Stabilizer simulator methods supported gateset.
For Aer's backend the jsonschema validation of input qobj objects from terra is now opt-in instead of being enabled by default. If you want to enable jsonschema validation of qobj set the
validatekwarg on theqiskit.providers.aer.QasmSimualtor.run()method for the backend object toTrue.Adds an OpSet object to the base simulator State class to allow easier validation of instructions, gates, and snapshots supported by simulators.
Refactor OpSet class. Moved OpSet to separate header file and add
containsanddifferencemethods based onstd::set::containsandstd::algorithm::set_difference. These replace the removed invalid and validate instructions from OpSet, but with the order reversed. It returns a list of other ops not in current opset rather than opset instructions not in the other.Improves how measurement sampling optimization is checked. The expensive part of this operation is now done once during circuit construction where rather than multiple times during simulation for when checking memory requirements, simulation method, and final execution.
Bug Fixes¶
Remove "extended_stabilizer" from the automatically selected simulation methods. This is needed as the extended stabilizer method is not exact and may give incorrect results for certain circuits unless the user knows how to optimize its configuration parameters.
The automatic method now only selects from "stabilizer", "density_matrix", and "statevector" methods. If a non-Clifford circuit that is too large for the statevector method is executed an exception will be raised suggesting you could try explicitly using the "extended_stabilizer" or "matrix_product_state" methods instead.
Disables gate fusion for the matrix product state simulation method as this was causing issues with incorrect results being returned in some cases.
Fixes a bug causing incorrect channel evaluation in the
qiskit.providers.aer.PulseSimulator.Fixes several minor bugs for Hamiltonian parsing edge cases in the
qiskit.providers.aer.pulse.system_models.hamiltonian_model.HamiltonianModelclass.
Ignis 0.4.0¶
Prelude¶
The main change made in this release is a refactor of the Randomized
Benchmarking code to integrate the updated Clifford class
qiskit.quantum_info.Clifford from Terra and to improve the
CNOT-Dihedral class.
New Features¶
The
qiskit.ignis.verification.randomized_benchmarking.randomized_benchmarking_seq()function was refactored to use the updated Clifford classClifford, to allow efficient Randomized Benchmarking (RB) on Clifford sequences with more than 2 qubits. In addition, the code of the CNOT-Dihedral classqiskit.ignis.verification.randomized_benchmarking.CNOTDihedralwas refactored to make it more efficient, by using numpy arrays, as well not using pre-generated pickle files storing all the 2-qubit group elements. Theqiskit.ignis.verification.randomized_benchmarking.randomized_benchmarking_seq()function has a new kwargrand_seedwhich can be used to specify a seed for the random number generator used to generate the RB circuits. This can be useful for having a reproducible circuit.The
qiskit.ignis.verification.qv_circuits()function has a new kwargseedwhich can be used to specify a seed for the random number generator used to generate the Quantum Volume circuits. This can be useful for having a reproducible circuit.
Upgrade Notes¶
The
qiskit.ignis.verification.randomized_benchmarking.randomized_benchmarking_seq()function is now using the updated Clifford classCliffordand the updated CNOT-Dihedral classqiskit.ignis.verification.randomized_benchmarking.CNOTDihedralto construct its output instead of using pre-generated group tables for the Clifford and CNOT-Dihedral group elements, which were stored in pickle files. This may result in subtle differences from the output from the previous version.A new requirement scikit-learn has been added to the requirements list. This dependency was added in the 0.3.0 release but wasn't properly exposed as a dependency in that release. This would lead to an
ImportErrorif theqiskit.ignis.measurement.discriminator.iq_discriminatorsmodule was imported. This is now correctly listed as a dependency so thatscikit-learnwill be installed with qiskit-ignis.The
qiskit.ignis.verification.qv_circuits()function is now using the circuit library classQuantumVolumeto construct its output instead of building the circuit from scratch. This may result in subtle differences from the output from the previous version.Tomography fitters can now also get list of Result objects instead of a single Result as requested in issue #320.
Deprecation Notes¶
The kwarg
interleaved_gatesfor theqiskit.ignis.verification.randomized_benchmarking.randomized_benchmarking_seq()function has been deprecated and will be removed in a future release. It is superseded byinterleaved_elem. The helper functionsqiskit.ignis.verification.randomized_benchmarking.BasicUtils,qiskit.ignis.verification.randomized_benchmarking.CliffordUtilsandqiskit.ignis.verification.randomized_benchmarking.DihedralUtilswere deprecated. These classes are superseded byqiskit.ignis.verification.randomized_benchmarking.RBgroupthat handles the group operations needed for RB. The classqiskit.ignis.verification.randomized_benchmarking.Cliffordis superseded byClifford.The kwargs
qrandcrfor theqiskit.ignis.verification.qv_circuits()function have been deprecated and will be removed in a future release. These kwargs were documented as being used for specifying aqiskit.circuit.QuantumRegisterandqiskit.circuit.ClassicalRegisterto use in the generated Quantum Volume circuits instead of creating new ones. However, the parameters were never actually respected and a new Register would always be created regardless of whether they were set or not. This behavior is unchanged and these kwargs still do not have any effect, but are being deprecated prior to removal to avoid a breaking change for users who may have been setting either.Support for passing in subsets of qubits as a list in the
qubit_listsparameter for theqiskit.ignis.verification.qv_circuits()function has been deprecated and will removed in a future release. In the past this was used to specify a layout to run the circuit on a device. In other words if you had a 5 qubit device and wanted to run a 2 qubit QV circuit on qubits 1, 3, and 4 of that device. You would pass in[1, 3, 4]as one of the lists inqubit_lists, which would generate a 5 qubit virtual circuit and have qv applied to qubits 1, 3, and 4 in that virtual circuit. However, this functionality is not necessary and overlaps with the concept ofinitial_layoutin the transpiler and whether a circuit has been embedded with a layout set. Moving forward instead you should just runtranspile()orexecute()with initial layout set to do this. For example, running the above example would become:from qiskit import execute from qiskit.ignis.verification import qv_circuits initial_layout = [1, 3, 4] qv_circs, _ = qv_circuits([list(range3)]) execute(qv_circuits, initial_layout=initial_layout)
Aqua 0.7.5¶
New Features¶
Removed soft dependency on CPLEX in ADMMOptimizer. Now default optimizers used by ADMMOptimizer are MinimumEigenOptimizer for QUBO problems and SlsqpOptimizer as a continuous optimizer. You can still use CplexOptimizer as an optimizer for ADMMOptimizer, but it should be set explicitly.
New Yahoo! finance provider created.
Introduced
QuadraticProgramConverterwhich is an abstract class for converters. Addedconvert/interpretmethods for converters instead ofencode/decode. Addedto_isingandfrom_isingtoQuadraticProgramclass. Moved all parameters fromconvertto constructor exceptname. Created setter/getter for converter parameters. Addedauto_define_penaltyandinterpretfor``LinearEqualityToPenalty``. Now error messages of converters are more informative.Added an SLSQP optimizer
qiskit.optimization.algorithms.SlsqpOptimizeras a wrapper of the corresponding SciPy optimization method. This is a classical optimizer, does not depend on quantum algorithms and may be used as a replacement forCobylaOptimizer.Cobyla optimizer has been modified to accommodate a multi start feature introduced in the SLSQP optimizer. By default, the optimizer does not run in the multi start mode.
The
SummedOpdoes a mathematically more correct check for equality, where expressions such asX + X == 2*XandX + Z == Z + Xevaluate toTrue.
Deprecation Notes¶
GSLS optimizer class deprecated
__init__parametermax_iterin favor ofmaxiter. SPSA optimizer class deprecated__init__parametermax_trialsin favor ofmaxiter. optimize_svm function deprecatedmax_itersparameter in favor ofmaxiter. ADMMParameters class deprecated__init__parametermax_iterin favor ofmaxiter.The ising convert classes
qiskit.optimization.converters.QuadraticProgramToIsingandqiskit.optimization.converters.IsingToQuadraticProgramhave been deprecated and will be removed in a future release. Instead theqiskit.optimization.QuadraticProgrammethodsto_ising()andfrom_ising()should be used instead.The
pprint_as_stringmethod forqiskit.optimization.QuadraticProgramhas been deprecated and will be removed in a future release. Instead you should just run.pprint_as_string()on the output fromto_docplex()The
prettyprintmethod forqiskit.optimization.QuadraticProgramhas been deprecated and will be removed in a future release. Instead you should just run.prettyprint()on the output fromto_docplex()
Bug Fixes¶
Changed in python version 3.8: On macOS, the spawn start method is now the default. The fork start method should be considered unsafe as it can lead to crashes in subprocesses. However P_BFGS doesn't support spawn, so we revert to single process. Refer to #1109 <https://github.com/Qiskit/qiskit-aqua/issues/1109> for more details.
Binding parameters in the
CircuitStateFndid not copy the value ofis_measurementand always setis_measurement=False. This has been fixed.Previously, SummedOp.to_matrix_op built a list MatrixOp's (with numpy matrices) and then summed them, returning a single MatrixOp. Some algorithms (for example vqe) require summing thousands of matrices, which exhausts memory when building the list of matrices. With this change, no list is constructed. Rather, each operand in the sum is converted to a matrix, added to an accumulator, and discarded.
Changing backends in VQE from statevector to qasm_simulator or real device was causing an error due to CircuitSampler incompatible reuse. VQE was changed to always create a new CircuitSampler and create a new expectation in case not entered by user. Refer to #1153 <https://github.com/Qiskit/qiskit-aqua/issues/1153> for more details.
Exchange and Wikipedia finance providers were fixed to correctly handle Quandl data. Refer to #775 <https://github.com/Qiskit/qiskit-aqua/issues/775> for more details. Fixes a divide by 0 error on finance providers mean vector and covariance matrix calculations. Refer to #781 <https://github.com/Qiskit/qiskit-aqua/issues/781> for more details.
The
ListOp.combo_fnproperty has been lost in several transformations, such as converting to another operator type, traversing, reducing or multiplication. Now this attribute is propagated to the resulting operator.The evaluation of some operator expressions, such as of
SummedOp``s and evaluations with the ``CircuitSamplerdid not treat coefficients correctly or ignored them completely. E.g. evaluating~StateFn(0 * (I + Z)) @ Plusdid not yield 0 or the normalization of~StateFn(I) @ ((Plus + Minus) / sqrt(2))missed a factor ofsqrt(2). This has been fixed.OptimizationResultincluded some public setters and class variables wereOptional. This fix makes all class variables read-only so that mypy and pylint can check types more effectively.MinimumEigenOptimizer.solvegenerated bitstrings in a result asstr. This fix changed the result intoList[float]as the other algorithms do. Some public classes related to optimization algorithms were missing in the documentation ofqiskit.optimization.algorithms. This fix added all such classes to the docstring. #1131 <https://github.com/Qiskit/qiskit-aqua/issues/1131> for more details.OptimizationResult.__init__did not check whether the sizes ofxandvariablesmatch or not (they should match). This fix added the check to raise an error if they do not match and fixes bugs detected by the check. This fix also adds missing unit tests related toOptimizationResult.variable_namesandOptimizationResult.variables_dictintest_converters. #1167 <https://github.com/Qiskit/qiskit-aqua/issues/1167> for more details.Fix parameter binding in the
OperatorStateFn, which did not bind parameters of the underlying primitive but just the coefficients.op.eval(other), whereopis of typeOperatorBase, sometimes silently returns a nonsensical value when the number of qubits inopandotherare not equal. This fix results in correct behavior, which is to throw an error rather than return a value, because the input in this case is invalid.The
construct_circuitmethod ofVQEpreviously returned the expectation value to be evaluated as typeOperatorBase. This functionality has been moved intoconstruct_expectationandconstruct_circuitreturns a list of the circuits that are evaluated to compute the expectation value.
IBM Q Provider 0.8.0¶
New Features¶
IBMQBackendnow has a newreservations()method that returns reservation information for the backend, with optional filtering. In addition, you can now useprovider.backends.my_reservations()to query for your own reservations.qiskit.providers.ibmq.job.IBMQJob.result()raises anIBMQJobFailureErrorexception if the job has failed. The exception message now contains the reason the job failed, if the entire job failed for a single reason.A new attribute
client_versionwas added toIBMQJobandqiskit.result.Resultobject retrieved viaqiskit.providers.ibmq.job.IBMQJob.result().client_versionis a dictionary with the key being the name and the value being the version of the client used to submit the job, such as Qiskit.The
least_busy()function now takes a new, optional parameterreservation_lookahead. If specified or defaulted to, a backend is considered unavailable if it has reservations in the nextnminutes, wherenis the value ofreservation_lookahead. For example, if the default value of 60 is used, then any backends that have reservations in the next 60 minutes are considered unavailable.ManagedResultsnow has a newcombine_results()method that combines results from all managed jobs and returns a singleResultobject. ThisResultobject can be used, for example, inqiskit-ignisfitter methods.
Upgrade Notes¶
Timestamps in the following fields are now in local time instead of UTC:
Backend properties returned by
qiskit.providers.ibmq.IBMQBackend.properties().Backend properties returned by
qiskit.providers.ibmq.job.IBMQJob.properties().estimated_start_timeandestimated_complete_timeinQueueInfo, returned byqiskit.providers.ibmq.job.IBMQJob.queue_info().dateinResult, returned byqiskit.providers.ibmq.job.IBMQJob.result().
In addition, the
datetimeparameter forqiskit.providers.ibmq.IBMQBackend.properties()is also expected to be in local time unless it has UTC timezone information.websockets8.0 or above is now required if Python 3.7 or above is used.websockets7.0 will continue to be used for Python 3.6 or below.On Windows, the event loop policy is set to
WindowsSelectorEventLoopPolicyinstead of using the defaultWindowsProactorEventLoopPolicy. This fixes the issue that theqiskit.providers.ibmq.job.IBMQJob.result()method could hang on Windows. Fixes #691
Deprecation Notes¶
Use of
Qconfig.pyto save IBM Quantum Experience credentials is deprecated and will be removed in the next release. You should useqiskitrc(the default) instead.
Bug Fixes¶
Fixes an issue wherein a call to
qiskit.providers.ibmq.IBMQBackend.jobs()can hang if the number of jobs being returned is large. Fixes #674Fixes an issue which would raise a
ValueErrorwhen building error maps in Jupyter for backends that are offline. Fixes #706qiskit.providers.ibmq.IBMQBackend.jobs()will now return the correct list ofIBMQJobobjects when thestatuskwarg is set to'RUNNING'.The package metadata has been updated to properly reflect the dependency on
qiskit-terra>= 0.14.0. This dependency was implicitly added as part of the 0.7.0 release but was not reflected in the package requirements so it was previously possible to installqiskit-ibmq-providerwith a version ofqiskit-terrawhich was too old. Fixes #677
Qiskit 0.19.6¶
Terra 0.14.2¶
No Change
Aer 0.5.2¶
No Change
Ignis 0.3.3¶
Upgrade Notes¶
A new requirement scikit-learn has been added to the requirements list. This dependency was added in the 0.3.0 release but wasn't properly exposed as a dependency in that release. This would lead to an
ImportErrorif theqiskit.ignis.measurement.discriminator.iq_discriminatorsmodule was imported. This is now correctly listed as a dependency so thatscikit-learnwill be installed with qiskit-ignis.
Bug Fixes¶
Fixes an issue in qiskit-ignis 0.3.2 which would raise an
ImportErrorwhenqiskit.ignis.verification.tomography.fitters.process_fitterwas imported withoutcvxpybeing installed.
Aqua 0.7.3¶
No Change
IBM Q Provider 0.7.2¶
No Change
Qiskit 0.19.5¶
Terra 0.14.2¶
No Change
Aer 0.5.2¶
No Change
Ignis 0.3.2¶
Bug Fixes¶
The
qiskit.ignis.verification.TomographyFitter.fit()method has improved detection logic for the default fitter. Previously, thecvxfitter method was used whenever cvxpy was installed. However, it was possible to install cvxpy without an SDP solver that would work for thecvxfitter method. This logic has been reworked so that thecvxfitter method is only used ifcvxpyis installed and an SDP solver is present that can be used. Otherwise, thelstsqfitter is used.Fixes an edge case in
qiskit.ignis.mitigation.measurement.fitters.MeasurementFitter.apply()for input that has invalid or incorrect state labels that don't match the calibration circuit. Previously, this would not error and just return an empty result. Instead now this case is correctly caught and aQiskitErrorexception is raised when using incorrect labels.
Aqua 0.7.3¶
Upgrade Notes¶
The cvxpy dependency which is required for the svm classifier has been removed from the requirements list and made an optional dependency. This is because installing cvxpy is not seamless in every environment and often requires a compiler be installed to run. To use the svm classifier now you'll need to install cvxpy by either running
pip install cvxpy<1.1.0or to install it with aqua runningpip install qiskit-aqua[cvx].
Bug Fixes¶
The
composemethod of theCircuitOpusedQuantumCircuit.combinewhich has been changed to useQuantumCircuit.compose. Using combine leads to the problem that composing an operator with aCircuitOpbased on a named register does not chain the operators but stacks them. E.g. composingZ ^ 2with a circuit based on a 2-qubit named register yielded a 4-qubit operator instead of a 2-qubit operator.The
MatrixOp.to_instructionmethod previously returned an operator and not an instruction. This method has been updated to return an Instruction. Note that this only works if the operator primitive is unitary, otherwise an error is raised upon the construction of the instruction.The
__hash__method of thePauliOpclass used theid()method which prevents set comparisons to work as expected since they rely on hash tables and identical objects used to not have identical hashes. Now, the implementation uses a hash of the string representation inline with the implementation in thePauliclass.
IBM Q Provider 0.7.2¶
No Change
Qiskit 0.19.4¶
Terra 0.14.2¶
Upgrade Notes¶
The
circuit_to_gateandcircuit_to_instructionconverters had previously automatically included the generated gate or instruction in the activeSessionEquivalenceLibrary. These converters now accept an optionalequivalence_librarykeyword argument to specify if and where the converted instances should be registered. The default behavior is not to register the converted instance.
Bug Fixes¶
Implementations of the multi-controlled X Gate (
MCXGrayCode,MCXRecursiveandMCXVChain) have had theirnameproperties changed to more accurately describe their implementation (mcx_gray,mcx_recursive, andmcx_vchainrespectively.) Previously, these gates shared the namemcx` with ``MCXGate, which caused these gates to be incorrectly transpiled and simulated.ControlledGateinstances with a setctrl_statewere in some cases not being evaluated as equal, even if the compared gates were equivalent. This has been resolved.Fixed the SI unit conversion for
qiskit.pulse.SetFrequency. TheSetFrequencyinstruction should be in Hz on the frontend and has to be converted to GHz whenSetFrequencyis converted toPulseQobjInstruction.Open controls were implemented by modifying a gate's definition. However, when the gate already exists in the basis, this definition is not used, which yields incorrect circuits sent to a backend. This modifies the unroller to output the definition if it encounters a controlled gate with open controls.
Aer 0.5.2¶
No Change
Ignis 0.3.0¶
No Change
Aqua 0.7.2¶
Prelude¶
VQE expectation computation with Aer qasm_simulator now defaults to a computation that has the expected shot noise behavior.
Upgrade Notes¶
cvxpy is now in the requirements list as a dependency for qiskit-aqua. It is used for the quadratic program solver which is used as part of the
qiskit.aqua.algorithms.QSVM. Previouslycvxoptwas an optional dependency that needed to be installed to use this functionality. This is no longer required as cvxpy will be installed with qiskit-aqua.For state tomography run as part of
qiskit.aqua.algorithms.HHLwith a QASM backend the tomography fitter functionqiskit.ignis.verification.StateTomographyFitter.fit()now gets called explicitly with the method set tolstsqto always use the least-squares fitting. Previously it would opportunistically try to use thecvxfitter ifcvxpywere installed. But, thecvxfitter depends on a specifically configuredcvxpyinstallation with an SDP solver installed as part ofcvxpywhich is not always present in an environment withcvxpyinstalled.The VQE expectation computation using qiskit-aer's
qiskit.providers.aer.extensions.SnapshotExpectationValueinstruction is not enabled by default anymore. This was changed to be the default in 0.7.0 because it is significantly faster, but it led to unexpected ideal results without shot noise (see #1013 for more details). The default has now changed back to match user expectations. Using the faster expectation computation is now opt-in by setting the newinclude_customkwarg toTrueon theqiskit.aqua.algorithms.VQEconstructor.
New Features¶
A new kwarg
include_customhas been added to the constructor forqiskit.aqua.algorithms.VQEand it's subclasses (mainlyqiskit.aqua.algorithms.QAOA). When set to true and theexpectationkwarg is set toNone(the default) this will enable the use of VQE expectation computation with Aer'sqasm_simulatorqiskit.providers.aer.extensions.SnapshotExpectationValueinstruction. The special Aer snapshot based computation is much faster but with the ideal output similar to state vector simulator.
IBM Q Provider 0.7.2¶
No Change
Qiskit 0.19.3¶
Terra 0.14.1¶
No Change
Aer 0.5.2¶
Bug Fixes¶
Fixed bug with statevector and unitary simulators running a number of (parallel) shots equal to the number of CPU threads instead of only running a single shot.
Fixes the "diagonal" qobj gate instructions being applied incorrectly in the density matrix Qasm Simulator method.
Fixes bug where conditional gates were not being applied correctly on the density matrix simulation method.
Fix bug in CZ gate and Z gate for "density_matrix_gpu" and "density_matrix_thrust" QasmSimulator methods.
Fixes issue where memory requirements of simulation were not being checked on the QasmSimulator when using a non-automatic simulation method.
Fixed a memory leak that effected the GPU simulator methods
Ignis 0.3.0¶
No Change
Aqua 0.7.1¶
No Change
IBM Q Provider 0.7.2¶
Bug Fixes¶
qiskit.provider.ibmq.IBMQBackend.jobs()will now return the correct list ofIBMQJobobjects when thestatuskwarg is set to'RUNNING'. Fixes #523The package metadata has been updated to properly reflect the dependency on
qiskit-terra>= 0.14.0. This dependency was implicitly added as part of the 0.7.0 release but was not reflected in the package requirements so it was previously possible to installqiskit-ibmq-providerwith a version ofqiskit-terrawhich was too old. Fixes #677
Qiskit 0.19.0¶
Terra 0.14.0¶
Prelude¶
The 0.14.0 release includes several new features and bug fixes. The biggest
change for this release is the introduction of a quantum circuit library
in qiskit.circuit.library, containing some circuit families of
interest.
The circuit library gives users access to a rich set of well-studied circuit families, instances of which can be used as benchmarks, as building blocks in building more complex circuits, or as a tool to explore quantum computational advantage over classical. The contents of this library will continue to grow and mature.
The initial release of the circuit library contains:
standard_gates: these are fixed-width gates commonly used as primitive building blocks, consisting of 1, 2, and 3 qubit gates. For example theXGate,RZZGateandCSWAPGate. The old location of these gates underqiskit.extensions.standardis deprecated.generalized_gates: these are families that can generalize to arbitrarily many qubits, for example aPermutationorGMS(Global Molmer-Sorensen gate).boolean_logic: circuits that transform basis states according to simple Boolean logic functions, such asADDorXOR.arithmetic: a set of circuits for doing classical arithmetic such asWeightedAdderandIntegerComparator.basis_changes: circuits such as the quantum Fourier transform,QFT, that mathematically apply basis changes.n_local: patterns to easily create large circuits with rotation and entanglement layers, such asTwoLocalwhich uses single-qubit rotations and two-qubit entanglements.data_preparation: circuits that take classical input data and encode it in a quantum state that is difficult to simulate, e.g.PauliFeatureMaporZZFeatureMap.Other circuits that have proven interesting in the literature, such as
QuantumVolume,GraphState, orIQP.
To allow easier use of these circuits as building blocks, we have introduced
a compose() method of
qiskit.circuit.QuantumCircuit for composition of circuits either
with other circuits (by welding them at the ends and optionally permuting
wires) or with other simpler gates:
>>> lhs.compose(rhs, qubits=[3, 2], inplace=True)
┌───┐ ┌─────┐ ┌───┐
lqr_1_0: ───┤ H ├─── rqr_0: ──■──┤ Tdg ├ lqr_1_0: ───┤ H ├───────────────
├───┤ ┌─┴─┐└─────┘ ├───┤
lqr_1_1: ───┤ X ├─── rqr_1: ┤ X ├─────── lqr_1_1: ───┤ X ├───────────────
┌──┴───┴──┐ └───┘ ┌──┴───┴──┐┌───┐
lqr_1_2: ┤ U1(0.1) ├ + = lqr_1_2: ┤ U1(0.1) ├┤ X ├───────
└─────────┘ └─────────┘└─┬─┘┌─────┐
lqr_2_0: ─────■───── lqr_2_0: ─────■───────■──┤ Tdg ├
┌─┴─┐ ┌─┴─┐ └─────┘
lqr_2_1: ───┤ X ├─── lqr_2_1: ───┤ X ├───────────────
└───┘ └───┘
lcr_0: 0 ═══════════ lcr_0: 0 ═══════════════════════
lcr_1: 0 ═══════════ lcr_1: 0 ═══════════════════════
With this, Qiskit's circuits no longer assume an implicit initial state of \(|0\rangle\), and will not be drawn with this initial state. The all-zero initial state is still assumed on a backend when a circuit is executed.
New Features¶
A new method,
has_entry(), has been added to theqiskit.circuit.EquivalenceLibraryclass to quickly check if a given gate has any known decompositions in the library.A new class
IQP, to construct an instantaneous quantum polynomial circuit, has been added to the circuit library moduleqiskit.circuit.library.A new
compose()method has been added toqiskit.circuit.QuantumCircuit. It allows composition of two quantum circuits without having to turn one into a gate or instruction. It also allows permutations of qubits/clbits at the point of composition, as well as optional inplace modification. It can also be used in place ofappend(), as it allows composing instructions and operators onto the circuit as well.qiskit.circuit.library.Diagonalcircuits have been added to the circuit library. These circuits implement diagonal quantum operators (consisting of non-zero elements only on the diagonal). They are more efficiently simulated by the Aer simulator than dense matrices.Add
from_label()method to theqiskit.quantum_info.Cliffordclass for initializing as the tensor product of single-qubit I, X, Y, Z, H, or S gates.Schedule transformer
qiskit.pulse.reschedule.compress_pulses()performs an optimization pass to reduce the usage of waveform memory in hardware by replacing multiple identical instances of a pulse in a pulse schedule with a single pulse. For example:from qiskit.pulse import reschedule schedules = [] for _ in range(2): schedule = Schedule() drive_channel = DriveChannel(0) schedule += Play(SamplePulse([0.0, 0.1]), drive_channel) schedule += Play(SamplePulse([0.0, 0.1]), drive_channel) schedules.append(schedule) compressed_schedules = reschedule.compress_pulses(schedules)
The
qiskit.transpiler.Layouthas a new methodreorder_bits()that is used to reorder a list of virtual qubits based on the layout object.Two new methods have been added to the
qiskit.providers.models.PulseBackendConfigurationfor interacting with channels.get_channel_qubits()to get a list of all qubits operated by the given channel andget_qubit_channel()to get a list of channels operating on the given qubit.
New
qiskit.extensions.HamiltonianGateandqiskit.circuit.QuantumCircuit.hamiltonian()methods are introduced, representing Hamiltonian evolution of the circuit wavefunction by a user-specified Hermitian Operator and evolution time. The evolution time can be aParameter, allowing the creation of parameterized UCCSD or QAOA-style circuits which compile toUnitaryGateobjects iftimeparameters are provided. The Unitary of aHamiltonianGatewith Hamiltonian OperatorHand time parametertis \(e^{-iHt}\).The circuit library module
qiskit.circuit.librarynow provides a new boolean logic AND circuit,qiskit.circuit.library.AND, and OR circuit,qiskit.circuit.library.OR, which implement the respective operations on a variable number of provided qubits.New fake backends are added under
qiskit.test.mock. These include mocked versions ofibmq_armonk,ibmq_essex,ibmq_london,ibmq_valencia,ibmq_cambridge,ibmq_paris,ibmq_rome, andibmq_athens. As with other fake backends, these include snapshots of calibration data (i.e.backend.defaults()) and error data (i.e.backend.properties()) taken from the real system, and can be used for local testing, compilation and simulation.The
last_update_dateparameter forBackendPropertiescan now also be passed in as adatetimeobject. Previously only a string in ISO8601 format was accepted.Adds
qiskit.quantum_info.Statevector.from_int()andqiskit.quantum_info.DensityMatrix.from_int()methods that allow constructing a computational basis state for specified system dimensions.The methods on the
qiskit.circuit.QuantumCircuitclass for adding gates (for exampleh()) which were previously added dynamically at run time to the class definition have been refactored to be statically defined methods of the class. This means that static analyzer (such as IDEs) can now read these methods.
Upgrade Notes¶
A new package, python-dateutil, is now required and has been added to the requirements list. It is being used to parse datetime strings received from external providers in
BackendPropertiesobjects.The marshmallow schema classes in
qiskit.providers.modelshave been removed since they are no longer used by the BackendObjects.The output of the
to_dict()method for the classes inqiskit.providers.modelsis no longer in a format for direct JSON serialization. Depending on the content contained in instances of these class there may be numpy arrays and/or complex numbers in the fields of the dict. If you're JSON serializing the output of the to_dict methods you should ensure your JSON encoder can handle numpy arrays and complex numbers. This includes:qiskit.providers.models.Nduv.to_dict()qiskit.providers.models.Gate.to_dict()
Deprecation Notes¶
The
qiskit.dagcircuit.DAGCircuit.compose()method now takes a list of qubits/clbits that specify the positional order of bits to compose onto. The dictionary-based method of mapping using theedge_mapargument is deprecated and will be removed in a future release.The
combine_into_edge_map()method for theqiskit.transpiler.Layoutclass has been deprecated and will be removed in a future release. Instead, the new methodreorder_bits()should be used to reorder a list of virtual qubits according to the layout object.Passing a
qiskit.pulse.ControlChannelobject in via the parameterchannelfor theqiskit.providers.models.PulseBackendConfigurationmethodcontrol()has been deprecated and will be removed in a future release. TheControlChannelobjects are now generated from the backend configurationchannelsattribute which has the information of all channels and the qubits they operate on. Now, the methodcontrol()is expected to take the parameterqubitsof the form(control_qubit, target_qubit)and typelistortuple, and returns a list of control channels.The
ANDandORmethods ofqiskit.circuit.QuantumCircuitare deprecated and will be removed in a future release. Instead you should use the circuit library boolean logic classesqiskit.circuit.library.ANDamdqiskit.circuit.library.ORand then append those objects to your class. For example:from qiskit import QuantumCircuit from qiskit.circuit.library import AND qc = QuantumCircuit(2) qc.h(0) qc.cx(0, 1) qc_and = AND(2) qc.compose(qc_and, inplace=True)
The
qiskit.extensions.standardmodule is deprecated and will be removed in a future release. The gate classes in that module have been moved toqiskit.circuit.library.standard_gates.
Bug Fixes¶
The
qiskit.circuit.QuantumCircuitmethodsinverse(),mirror()methods, as well as theQuantumCircuit.datasetter would generate an invalid circuit when used on a parameterized circuit instance. This has been resolved and these methods should now work with a parameterized circuit. Fixes #4235Previously when creating a controlled version of a standard qiskit gate if a
ctrl_statewas specified a genericControlledGateobject would be returned whereas without it a standard qiskit controlled gate would be returned if it was defined. This PR allows standard qiskit controlled gates to understandctrl_state.Additionally, this PR fixes what might be considered a bug where setting the
ctrl_stateof an already controlled gate would assume the specified state applied to the full control width instead of the control qubits being added. For instance,:circ = QuantumCircuit(2) circ.h(0) circ.x(1) gate = circ.to_gate() cgate = gate.control(1) c3gate = cgate.control(2, ctrl_state=0)
would apply
ctrl_stateto all three control qubits instead of just the two control qubits being added.Fixed a bug in
random_clifford()that stopped it from sampling the full Clifford group. Fixes #4271The
qiskit.circuit.Instructionmethodqiskit.circuit.Instruction.is_parameterized()method had previously returnedTruefor anyInstructioninstance which had aqiskit.circuit.Parameterin any element of itsparamsarray, even if thatParameterhad been fully bound. This has been corrected so that.is_parameterizedwill returnFalsewhen the instruction is fully bound.qiskit.circuit.ParameterExpression.subs()had not correctly detected some cases where substituting parameters would result in a two distinctParametersobjects in an expression with the same name. This has been corrected so aCircuitErrorwill be raised in these cases.Improve performance of
qiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixfor low-qubit circuit simulations by optimizing the class__init__methods. Fixes #4281The function
qiskit.compiler.transpile()now correctly handles when the parameterbasis_gatesis set toNone. This will allow any gate in the output tranpiled circuit, including gates added by the transpilation process. Note that using this parameter may have some unintended consequences during optimization. Some transpiler passes depend on having abasis_gatesset. For example,qiskit.transpiler.passes.Optimize1qGatesonly optimizes the chains of u1, u2, and u3 gates and withoutbasis_gatesit is unable to unroll gates that otherwise could be optimized:from qiskit import * q = QuantumRegister(1, name='q') circuit = QuantumCircuit(q) circuit.h(q[0]) circuit.u1(0.1, q[0]) circuit.u2(0.1, 0.2, q[0]) circuit.h(q[0]) circuit.u3(0.1, 0.2, 0.3, q[0]) result = transpile(circuit, basis_gates=None, optimization_level=3) result.draw()
┌───┐┌─────────────┐┌───┐┌─────────────────┐ q_0: ┤ H ├┤ U2(0.1,0.3) ├┤ H ├┤ U3(0.1,0.2,0.3) ├ └───┘└─────────────┘└───┘└─────────────────┘Fixes #3017
Other Notes¶
The objects in
qiskit.providers.modelswhich were previously constructed using the marshmallow library have been refactored to not depend on marshmallow. This includes:NduvGate
These should be drop-in replacements without any noticeable change but specifics inherited from marshmallow may not work. Please file issues for any incompatibilities found.
Aer 0.5.1¶
No Change
Ignis 0.3.0¶
No Change
Aqua 0.7.0¶
Prelude¶
The Qiskit Aqua 0.7.0 release introduces a lot of new functionality along
with an improved integration with qiskit.circuit.QuantumCircuit
objects. The central contributions are the Qiskit's optimization module,
a complete refactor on Operators, using circuits as native input for the
algorithms and removal of the declarative JSON API.
Optimization module¶
The qiskit.optimization` module now offers functionality for modeling
and solving quadratic programs. It provides various near-term quantum and
conventional algorithms, such as the MinimumEigenOptimizer
(covering e.g. VQE or QAOA) or CplexOptimizer, as well as
a set of converters to translate between different
problem representations, such as QuadraticProgramToQubo.
See the
changelog
for a list of the added features.
Operator flow¶
The operator logic provided in qiskit.aqua.operators` was completely
refactored and is now a full set of tools for constructing
physically-intuitive quantum computations. It contains state functions,
operators and measurements and internally relies on Terra's Operator
objects. Computing expectation values and evolutions was heavily simplified
and objects like the ExpectationFactory produce the suitable, most
efficient expectation algorithm based on the Operator input type.
See the changelog
for a overview of the added functionality.
Native circuits¶
Algorithms commonly use parameterized circuits as input, for example the
VQE, VQC or QSVM. Previously, these inputs had to be of type
VariationalForm or FeatureMap which were wrapping the circuit
object. Now circuits are natively supported in these algorithms, which
means any individually constructed QuantumCircuit can be passed to
these algorithms. In combination with the release of the circuit library
which offers a wide collection of circuit families, it is now easy to
construct elaborate circuits as algorithm input.
Declarative JSON API¶
The ability of running algorithms using dictionaries as parameters as well as using the Aqua interfaces GUI has been removed.
IBM Q Provider 0.7.0¶
New Features¶
A new exception,
qiskit.providers.ibmq.IBMQBackendJobLimitError, is now raised if a job could not be submitted because the limit on active jobs has been reached.qiskit.providers.ibmq.job.IBMQJobandqiskit.providers.ibmq.managed.ManagedJobSeteach has two new methodsupdate_nameandupdate_tags. They are used to change the name and tags of a job or a job set, respectively.qiskit.providers.ibmq.IBMQFactory.save_account()andqiskit.providers.ibmq.IBMQFactory.enable_account()now accept optional parametershub,group, andproject, which allow specifying a default provider to save to disk or use, respectively.
Upgrade Notes¶
The
qiskit.providers.ibmq.job.IBMQJobmethodscreation_dateandtime_per_stepnow return date time information as adatetimeobject in local time instead of UTC. Similarly, the parametersstart_datetimeandend_datetime, ofqiskit.providers.ibmq.IBMQBackendService.jobs()andqiskit.providers.ibmq.IBMQBackend.jobs()can now be specified in local time.The
qiskit.providers.ibmq.job.QueueInfo.format()method now uses a customdatetimeto string formatter, and the package arrow is no longer required and has been removed from the requirements list.
Deprecation Notes¶
The
from_dict()andto_dict()methods ofqiskit.providers.ibmq.job.IBMQJobare deprecated and will be removed in the next release.
Bug Fixes¶
Fixed an issue where
nest_asyncio.apply()may raise an exception if there is no asyncio loop due to threading.
Qiskit 0.18.3¶
Terra 0.13.0¶
No Change
Aer 0.5.1¶
Upgrade Notes¶
Changes how transpilation passes are handled in the C++ Controller classes so that each pass must be explicitly called. This allows for greater customization on when each pass should be called, and with what parameters. In particular this enables setting different parameters for the gate fusion optimization pass depending on the QasmController simulation method.
Add
gate_length_unitskwarg toqiskit.providers.aer.noise.NoiseModel.from_device()for specifying customgate_lengthsin the device noise model function to handle unit conversions for internal code.Add Controlled-Y ("cy") gate to the Stabilizer simulator methods supported gateset.
For Aer's backend the jsonschema validation of input qobj objects from terra is now opt-in instead of being enabled by default. If you want to enable jsonschema validation of qobj set the
validatekwarg on theqiskit.providers.aer.QasmSimualtor.run()method for the backend object toTrue.
Bug Fixes¶
Remove "extended_stabilizer" from the automatically selected simulation methods. This is needed as the extended stabilizer method is not exact and may give incorrect results for certain circuits unless the user knows how to optimize its configuration parameters.
The automatic method now only selects from "stabilizer", "density_matrix", and "statevector" methods. If a non-Clifford circuit that is too large for the statevector method is executed an exception will be raised suggesting you could try explicitly using the "extended_stabilizer" or "matrix_product_state" methods instead.
Fixes Controller classes so that the ReduceBarrier transpilation pass is applied first. This prevents barrier instructions from preventing truncation of unused qubits if the only instruction defined on them was a barrier.
Disables gate fusion for the matrix product state simulation method as this was causing issues with incorrect results being returned in some cases.
Fix error in gate time unit conversion for device noise model with thermal relaxation errors and gate errors. The error probability the depolarizing error was being calculated with gate time in microseconds, while for thermal relaxation it was being calculated in nanoseconds. This resulted in no depolarizing error being applied as the incorrect units would make the device seem to be coherence limited.
Fix bug in incorrect composition of QuantumErrors when the qubits of composed instructions differ.
Fix issue where the "diagonal" gate is checked to be unitary with too high a tolerance. This was causing diagonals generated from Numpy functions to often fail the test.
Fix remove-barrier circuit optimization pass to be applied before qubit trucation. This fixes an issue where barriers inserted by the Terra transpiler across otherwise inactive qubits would prevent them from being truncated.
Ignis 0.3.0¶
No Change
Aqua 0.6.6¶
No Change
IBM Q Provider 0.6.1¶
No Change
Qiskit 0.18.0¶
Terra 0.13.0¶
Prelude¶
The 0.13.0 release includes many big changes. Some highlights for this release are:
For the transpiler we have switched the graph library used to build the
qiskit.dagcircuit.DAGCircuit class which is the underlying data
structure behind all operations to be based on
retworkx for greatly improved
performance. Circuit transpilation speed in the 0.13.0 release should
be significanlty faster than in previous releases.
There has been a significant simplification to the style in which Pulse
instructions are built. Now, Command s are deprecated and a unified
set of Instruction s are supported.
The qiskit.quantum_info module includes several new functions
for generating random operators (such as Cliffords and quantum channels)
and for computing the diamond norm of quantum channels; upgrades to the
Statevector and
DensityMatrix classes to support
computing measurement probabilities and sampling measurements; and several
new classes are based on the symplectic representation
of Pauli matrices. These new classes include Clifford operators
(Clifford), N-qubit matrices that are
sparse in the Pauli basis (SparsePauliOp),
lists of Pauli's (PauliTable),
and lists of stabilizers (StabilizerTable).
This release also has vastly improved documentation across Qiskit,
including improved documentation for the qiskit.circuit,
qiskit.pulse and qiskit.quantum_info modules.
Additionally, the naming of gate objects and
QuantumCircuit methods have been updated to be
more consistent. This has resulted in several classes and methods being
deprecated as things move to a more consistent naming scheme.
For full details on all the changes made in this release see the detailed release notes below.
New Features¶
Added a new circuit library module
qiskit.circuit.library. This will be a place for constructors of commonly used circuits that can be used as building blocks for larger circuits or applications.The
qiskit.providers.BaseJobclass has four new methods:These methods are used to check wheter a job is in a given job status.
Add ability to specify control conditioned on a qubit being in the ground state. The state of the control qubits is represented by an integer. For example:
from qiskit import QuantumCircuit from qiskit.extensions.standard import XGate qc = QuantumCircuit(4) cgate = XGate().control(3, ctrl_state=6) qc.append(cgate, [0, 1, 2, 3])
Creates a four qubit gate where the fourth qubit gets flipped if the first qubit is in the ground state and the second and third qubits are in the excited state. If
ctrl_stateisNone, the default, control is conditioned on all control qubits being excited.A new jupyter widget,
%circuit_library_infohas been added toqiskit.tools.jupyter. This widget is used for visualizing details about circuits built from the circuit library. For examplefrom qiskit.circuit.library import XOR import qiskit.tools.jupyter circuit = XOR(5, seed=42) %circuit_library_info circuit
A new kwarg option,
formatted, has been added toqiskit.circuit.QuantumCircuit.qasm(). When set toTruethe method will print a syntax highlighted version (using pygments) to stdout and returnNone(which differs from the normal behavior of returning the QASM code as a string).A new kwarg option,
filename, has been added toqiskit.circuit.QuantumCircuit.qasm(). When set to a path the method will write the QASM code to that file. It will then continue to output as normal.A new instruction
SetFrequencywhich allows users to change the frequency of thePulseChannel. This is done in the following way:from qiskit.pulse import Schedule from qiskit.pulse import SetFrequency sched = pulse.Schedule() sched += SetFrequency(5.5e9, DriveChannel(0))
In this example, the frequency of all pulses before the
SetFrequencycommand will be the default frequency and all pulses applied to drive channel zero after theSetFrequencycommand will be at 5.5 GHz. Users ofSetFrequencyshould keep in mind any hardware limitations.A new method,
assign_parameters()has been added to theqiskit.circuit.QuantumCircuitclass. This method accepts a parameter dictionary with both floats and Parameters objects in a single dictionary. In other words this new method allows you to bind floats, Parameters or both in a single dictionary.Also, by using the
inplacekwarg it can be specified you can optionally modify the original circuit in place. By default this is set toFalseand a copy of the original circuit will be returned from the method.A new method
num_nonlocal_gates()has been added to theqiskit.circuit.QuantumCircuitclass. This method will return the number of gates in a circuit that involve 2 or or more qubits. These gates are more costly in terms of time and error to implement.The
qiskit.circuit.QuantumCircuitmethodiso()for adding anIsometrygate to the circuit has a new alias. You can now callqiskit.circuit.QuantumCircuit.isometry()in addition to callingiso.A
descriptionattribute has been added to theCouplingMapclass for storing a short description for different coupling maps (e.g. full, grid, line, etc.).A new method
compose()has been added to theDAGCircuitclass for composing two circuits via their DAGs.dag_left.compose(dag_right, edge_map={right_qubit0: self.left_qubit1, right_qubit1: self.left_qubit4, right_clbit0: self.left_clbit1, right_clbit1: self.left_clbit0})
┌───┐ ┌─────┐┌─┐ lqr_1_0: ───┤ H ├─── rqr_0: ──■──┤ Tdg ├┤M├ ├───┤ ┌─┴─┐└─┬─┬─┘└╥┘ lqr_1_1: ───┤ X ├─── rqr_1: ┤ X ├──┤M├───╫─ ┌──┴───┴──┐ └───┘ └╥┘ ║ lqr_1_2: ┤ U1(0.1) ├ + rcr_0: ════════╬════╩═ = └─────────┘ ║ lqr_2_0: ─────■───── rcr_1: ════════╩══════ ┌─┴─┐ lqr_2_1: ───┤ X ├─── └───┘ lcr_0: ═══════════ lcr_1: ═══════════ ┌───┐ lqr_1_0: ───┤ H ├────────────────── ├───┤ ┌─────┐┌─┐ lqr_1_1: ───┤ X ├─────■──┤ Tdg ├┤M├ ┌──┴───┴──┐ │ └─────┘└╥┘ lqr_1_2: ┤ U1(0.1) ├──┼──────────╫─ └─────────┘ │ ║ lqr_2_0: ─────■───────┼──────────╫─ ┌─┴─┐ ┌─┴─┐ ┌─┐ ║ lqr_2_1: ───┤ X ├───┤ X ├──┤M├───╫─ └───┘ └───┘ └╥┘ ║ lcr_0: ═══════════════════╩════╬═ ║ lcr_1: ════════════════════════╩═The mock backends in
qiskit.test.mocknow have a functionalrun()method that will return results similar to the real devices. Ifqiskit-aeris installed a simulation will be run with a noise model built from the device snapshot in the fake backend. Otherwise,qiskit.providers.basicaer.QasmSimulatorPywill be used to run an ideal simulation. Additionally, if a pulse experiment is passed torunand qiskit-aer is installed thePulseSimulatorwill be used to simulate the pulse schedules.The
qiskit.result.Result()methodget_counts()will now return a list of all the counts available when there are multiple circuits in a job. This works whenget_counts()is called with no arguments.The main consideration for this feature was for drawing all the results from multiple circuits in the same histogram. For example it is now possible to do something like:
from qiskit import execute from qiskit import QuantumCircuit from qiskit.providers.basicaer import BasicAer from qiskit.visualization import plot_histogram sim = BasicAer.get_backend('qasm_simulator') qc = QuantumCircuit(2) qc.h(0) qc.cx(0, 1) qc.measure_all() result = execute([qc, qc, qc], sim).result() plot_histogram(result.get_counts())
A new kwarg,
initial_statehas been added to theqiskit.visualization.circuit_drawer()function and theQuantumCircuitmethoddraw(). When set toTruethe initial state will be included in circuit visualizations for all backends. For example:from qiskit import QuantumCircuit circuit = QuantumCircuit(2) circuit.measure_all() circuit.draw(output='mpl', initial_state=True)
It is now possible to insert a callable into a
qiskit.pulse.InstructionScheduleMapwhich returns a newqiskit.pulse.Schedulewhen it is called with parameters. For example:def test_func(x): sched = Schedule() sched += pulse_lib.constant(int(x), amp_test)(DriveChannel(0)) return sched inst_map = InstructionScheduleMap() inst_map.add('f', (0,), test_func) output_sched = inst_map.get('f', (0,), 10) assert output_sched.duration == 10
Two new gate classes,
qiskit.extensions.iSwapGateandqiskit.extensions.DCXGate, along with theirQuantumCircuitmethodsiswap()anddcx()have been added to the standard extensions. These gates, which are locally equivalent to each other, can be used to enact particular XY interactions. A brief motivation for these gates can be found in: arxiv.org/abs/quant-ph/0209035The
qiskit.providers.BaseJobclass now has a new methodwait_for_final_state()that polls for the job status until the job reaches a final state (such asDONEorERROR). This method also takes an optionalcallbackkwarg which takes a Python callable that will be called during each iteration of the poll loop.The
search_widthandsearch_depthattributes of theqiskit.transpiler.passes.LookaheadSwappass are now settable when initializing the pass. A larger search space can often lead to more optimized circuits, at the cost of longer run time.The number of qubits in
BackendConfigurationcan now be accessed via the propertynum_qubits. It was previously only accessible via then_qubitsattribute.Two new methods,
angles()andangles_and_phase(), have been added to theqiskit.quantum_info.OneQubitEulerDecomposerclass. These methods will return the relevant parameters without validation, and calling theOneQubitEulerDecomposerobject will perform the full synthesis with validation.An
RRdecomposition basis has been added to theqiskit.quantum_info.OneQubitEulerDecomposerfor decomposing an arbitrary 2x2 unitary into a twoRGatecircuit.Adds the ability to set
qargsto objects which are subclasses of the abstractBaseOperatorclass. This is done by calling the objectop(qargs)(whereopis an operator class) and will return a shallow copy of the original object with a qargs property set. When such an object is used with thecompose()ordot()methods the internal value for qargs will be used when theqargsmethod kwarg is not used. This allows for subsystem composition using binary operators, for example:from qiskit.quantum_info import Operator init = Operator.from_label('III') x = Operator.from_label('X') h = Operator.from_label('H') init @ x([0]) @ h([1])
Adds
qiskit.quantum_info.Cliffordoperator class to the quantum_info module. This operator is an efficient symplectic representation an N-qubit unitary operator from the Clifford group. This class includes ato_circuit()method for compilation into aQuantumCircuitof Clifford gates with a minimal number of CX gates for up to 3-qubits. It also providers general compilation for N > 3 qubits but this method is not optimal in the number of two-qubit gates.Adds
qiskit.quantum_info.SparsePauliOpoperator class. This is an efficient representaiton of an N-qubit matrix that is sparse in the Pauli basis and uses aqiskit.quantum_info.PauliTableand vector of complex coefficients for its data structure.This class supports much of the same functionality of the
qiskit.quantum_info.Operatorclass soSparsePauliOpobjects can be tensored, composed, scalar multiplied, added and subtracted.Numpy arrays or
Operatorobjects can be converted to aSparsePauliOpusing the :class:`~qiskit.quantum_info.SparsePauliOp.from_operator method.SparsePauliOpcan be convered to a sparse csr_matrix or dense Numpy array using theto_matrixmethod, or to anOperatorobject using theto_operatormethod.A
SparsePauliOpcan be iterated over in terms of itsPauliTablecomponents and coefficients, its coefficients and Pauli string labels using thelabel_iter()method, and the (dense or sparse) matrix components using thematrix_iter()method.Add
qiskit.quantum_info.diamond_norm()function for computing the diamond norm (completely-bounded trace-norm) of a quantum channel. This can be used to compute the distance between two quantum channels usingdiamond_norm(chan1 - chan2).A new class
qiskit.quantum_info.PauliTablehas been added. This is an efficient symplectic representation of a list of N-qubit Pauli operators. Some features of this class are:PauliTableobjects may be composed, and tensored which will return aPauliTableobject with the combination of the operation (compose(),dot(),expand(),tensor()) between each element of the first table, with each element of the second table.Addition of two tables acts as list concatination of the terms in each table (
+).Pauli tables can be sorted by lexicographic (tensor product) order or by Pauli weights (
sort()).Duplicate elements can be counted and deleted (
unique()).The PauliTable may be iterated over in either its native symplectic boolean array representation, as Pauli string labels (
label_iter()), or as dense Numpy array or sparse CSR matrices (matrix_iter()).Checking commutation between elements of the Pauli table and another Pauli (
commutes()) or Pauli table (commutes_with_all())
See the
qiskit.quantum_info.PauliTableclass API documentation for additional details.Adds
qiskit.quantum_info.StabilizerTableclass. This is a subclass of theqiskit.quantum_info.PauliTableclass which includes a boolean phase vector along with the Pauli table array. This represents a list of Stabilizer operators which are real-Pauli operators with +1 or -1 coefficient. Because the stabilizer matrices are real the"Y"label matrix is defined as[[0, 1], [-1, 0]]. See the API documentation for additional information.Adds
qiskit.quantum_info.pauli_basis()function which returns an N-qubit Pauli basis as aqiskit.quantum_info.PauliTableobject. The ordering of this basis can either be by standard lexicographic (tensor product) order, or by the number of non-identity Pauli terms (weight).Adds
qiskit.quantum_info.ScalarOpoperator class that represents a scalar multiple of an identity operator. This can be used to initialize an identity on arbitrary dimension subsystems and it will be implicitly converted to otherBaseOperatorsubclasses (such as anqiskit.quantum_info.Operatororqiskit.quantum_info.SuperOp) when it is composed with, or added to, them.Example: Identity operator
from qiskit.quantum_info import ScalarOp, Operator X = Operator.from_label('X') Z = Operator.from_label('Z') init = ScalarOp(2 ** 3) # 3-qubit identity op = init @ X([0]) @ Z([1]) @ X([2]) # Op XZX
A new method,
reshape(), has been added to theqiskit.quantum_innfo.Operatorclass that returns a shallow copy of an operator subclass with reshaped subsystem input or output dimensions. The combined dimensions of all subsystems must be the same as the original operator or an exception will be raised.Adds
qiskit.quantum_info.random_clifford()for generating a randomqiskit.quantum_info.Cliffordoperator.Add
qiskit.quantum_info.random_quantum_channel()function for generating a random quantum channel with fixedChoi-rank in theStinespringrepresentation.Add
qiskit.quantum_info.random_hermitian()for generating a random HermitianOperator.Add
qiskit.quantum_info.random_statevector()for generating a randomStatevector.Adds
qiskit.quantum_info.random_pauli_table()for generating a randomqiskit.quantum_info.PauliTable.Adds
qiskit.quantum_info.random_stabilizer_table()for generating a randomqiskit.quantum_info.StabilizerTable.Add a
num_qubitsattribute toqiskit.quantum_info.StateVectorandqiskit.quantum_info.DensityMatrixclasses. This returns the number of qubits for N-qubit states and returnsNonefor non-qubit states.Adds
to_dict()andto_dict()methods to convertqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixobjects into Bra-Ket notation dictionary.Example
from qiskit.quantum_info import Statevector state = Statevector.from_label('+0') print(state.to_dict())
{'00': (0.7071067811865475+0j), '10': (0.7071067811865475+0j)}from qiskit.quantum_info import DensityMatrix state = DensityMatrix.from_label('+0') print(state.to_dict())
{'00|00': (0.4999999999999999+0j), '10|00': (0.4999999999999999+0j), '00|10': (0.4999999999999999+0j), '10|10': (0.4999999999999999+0j)}Adds
probabilities()andprobabilities()toqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixclasses which return an array of measurement outcome probabilities in the computational basis for the specified subsystems.Example
from qiskit.quantum_info import Statevector state = Statevector.from_label('+0') print(state.probabilities())
[0.5 0. 0.5 0. ]
from qiskit.quantum_info import DensityMatrix state = DensityMatrix.from_label('+0') print(state.probabilities())
[0.5 0. 0.5 0. ]
Adds
probabilities_dict()andprobabilities_dict()toqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixclasses which return a count-style dictionary array of measurement outcome probabilities in the computational basis for the specified subsystems.from qiskit.quantum_info import Statevector state = Statevector.from_label('+0') print(state.probabilities_dict())
{'00': 0.4999999999999999, '10': 0.4999999999999999}from qiskit.quantum_info import DensityMatrix state = DensityMatrix.from_label('+0') print(state.probabilities_dict())
{'00': 0.4999999999999999, '10': 0.4999999999999999}Add
sample_counts()andsample_memory()methods to theStatevectorandDensityMatrixclasses for sampling measurement outcomes on subsystems.Example:
Generate a counts dictionary by sampling from a statevector
from qiskit.quantum_info import Statevector psi = Statevector.from_label('+0') shots = 1024 # Sample counts dictionary counts = psi.sample_counts(shots) print('Measure both:', counts) # Qubit-0 counts0 = psi.sample_counts(shots, [0]) print('Measure Qubit-0:', counts0) # Qubit-1 counts1 = psi.sample_counts(shots, [1]) print('Measure Qubit-1:', counts1)
Measure both: {'00': 525, '10': 499} Measure Qubit-0: {'0': 1024} Measure Qubit-1: {'0': 509, '1': 515}Return the array of measurement outcomes for each sample
from qiskit.quantum_info import Statevector psi = Statevector.from_label('-1') shots = 10 # Sample memory mem = psi.sample_memory(shots) print('Measure both:', mem) # Qubit-0 mem0 = psi.sample_memory(shots, [0]) print('Measure Qubit-0:', mem0) # Qubit-1 mem1 = psi.sample_memory(shots, [1]) print('Measure Qubit-1:', mem1)
Measure both: ['11' '01' '11' '11' '11' '11' '01' '11' '11' '01'] Measure Qubit-0: ['1' '1' '1' '1' '1' '1' '1' '1' '1' '1'] Measure Qubit-1: ['1' '0' '1' '1' '0' '1' '1' '1' '0' '0']
Adds a
measure()method to theqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixquantum state classes. This allows sampling a single measurement outcome from the specified subsystems and collapsing the statevector to the post-measurement computational basis state. For examplefrom qiskit.quantum_info import Statevector psi = Statevector.from_label('+1') # Measure both qubits outcome, psi_meas = psi.measure() print("measure([0, 1]) outcome:", outcome, "Post-measurement state:") print(psi_meas) # Measure qubit-1 only outcome, psi_meas = psi.measure([1]) print("measure([1]) outcome:", outcome, "Post-measurement state:") print(psi_meas)
measure([0, 1]) outcome: 01 Post-measurement state: Statevector([0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j], dims=(2, 2)) measure([1]) outcome: 0 Post-measurement state: Statevector([-0.+0.j, 1.+0.j, -0.+0.j, 0.+0.j], dims=(2, 2))Adds a
reset()method to theqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixquantum state classes. This allows reseting some or all subsystems to the \(|0\rangle\) state. For examplefrom qiskit.quantum_info import Statevector psi = Statevector.from_label('+1') # Reset both qubits psi_reset = psi.reset() print("Post reset state: ") print(psi_reset) # Reset qubit-1 only psi_reset = psi.reset([1]) print("Post reset([1]) state: ") print(psi_reset)
Post reset state: Statevector([1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], dims=(2, 2)) Post reset([1]) state: Statevector([-0.+0.j, 1.+0.j, -0.+0.j, 0.+0.j], dims=(2, 2))A new visualization function
qiskit.visualization.visualize_transition()for visualizing single qubit gate transitions has been added. It takes in a single qubit circuit and returns an animation of qubit state transitions on a Bloch sphere. To use this function you must have installed the dependencies for and configured globally a matplotlib animtion writer. You can refer to the matplotlib documentation for more details on this. However, in the default case simply ensuring that FFmpeg is installed is sufficient to use this function.It supports circuits with the following gates:
HGateXGateYGateZGateRXGateRYGateRZGateSGateSdgGateTGateTdgGateU1Gate
For example:
from qiskit.visualization import visualize_transition from qiskit import * qc = QuantumCircuit(1) qc.h(0) qc.ry(70,0) qc.rx(90,0) qc.rz(120,0) visualize_transition(qc, fpg=20, spg=1, trace=True)
execute()has a new kwargschedule_circuit. By settingschedule_circuit=Truethis enables scheduling of the circuit into aSchedule. This allows users buildingqiskit.circuit.QuantumCircuitobjects to make use of custom scheduler methods, such as theas_late_as_possibleandas_soon_as_possiblemethods. For example:job = execute(qc, backend, schedule_circuit=True, scheduling_method="as_late_as_possible")
A new environment variable
QISKIT_SUPPRESS_PACKAGING_WARNINGScan be set toYorywhich will suppress the warnings aboutqiskit-aerandqiskit-ibmq-providernot being installed at import time. This is useful for users who are only running qiskit-terra (or just not qiskit-aer and/or qiskit-ibmq-provider) and the warnings are not an indication of a potential packaging problem. You can set the environment variable toNornto ensure that warnings are always enabled even if the user config file is set to disable them.A new user config file option,
suppress_packaging_warningshas been added. When set totruein your user config file like:[default] suppress_packaging_warnings = true
it will suppress the warnings about
qiskit-aerandqiskit-ibmq-providernot being installed at import time. This is useful for users who are only running qiskit-terra (or just not qiskit-aer and/or qiskit-ibmq-provider) and the warnings are not an indication of a potential packaging problem. If the user config file is set to disable the warnings this can be overriden by setting theQISKIT_SUPPRESS_PACKAGING_WARNINGStoNornqiskit.compiler.transpile()has two new kwargs,layout_methodandrouting_method. These allow you to select a particular method for placement and routing of circuits on constrained architectures. For, example:transpile(circ, backend, layout_method='dense', routing_method='lookahead')
will run
DenseLayoutlayout pass andLookaheadSwaprouting pass.There has been a significant simplification to the style in which Pulse instructions are built.
With the previous style,
Commands were called with channels to make anInstruction. The usage of both commands and instructions was a point of confusion. This was the previous style:sched += Delay(5)(DriveChannel(0)) sched += ShiftPhase(np.pi)(DriveChannel(0)) sched += SamplePulse([1.0, ...])(DriveChannel(0)) sched += Acquire(100)(AcquireChannel(0), MemorySlot(0))
or, equivalently (though less used):
sched += DelayInstruction(Delay(5), DriveChannel(0)) sched += ShiftPhaseInstruction(ShiftPhase(np.pi), DriveChannel(0)) sched += PulseInstruction(SamplePulse([1.0, ...]), DriveChannel(0)) sched += AcquireInstruction(Acquire(100), AcquireChannel(0), MemorySlot(0))
Now, rather than build a command and an instruction, each command has been migrated into an instruction:
sched += Delay(5, DriveChannel(0)) sched += ShiftPhase(np.pi, DriveChannel(0)) sched += Play(SamplePulse([1.0, ...]), DriveChannel(0)) sched += SetFrequency(5.5, DriveChannel(0)) # New instruction! sched += Acquire(100, AcquireChannel(0), MemorySlot(0))
There is now a
Playinstruction which takes a description of a pulse envelope and a channel. There is a newPulseclass in thepulse_libfrom which the pulse envelope description should subclass.For example:
Play(SamplePulse([0.1]*10), DriveChannel(0)) Play(ConstantPulse(duration=10, amp=0.1), DriveChannel(0))
Upgrade Notes¶
The
qiskit.dagcircuit.DAGNodemethodpopwhich was deprecated in the 0.9.0 release has been removed. If you were using this method you can leverage Python'sdelstatement ordelattr()function to perform the same task.A new optional visualization requirement, pygments , has been added. It is used for providing syntax highlighting of OpenQASM 2.0 code in Jupyter widgets and optionally for the
qiskit.circuit.QuantumCircuit.qasm()method. It must be installed (either withpip install pygmentsorpip install qiskit-terra[visualization]) prior to using the%circuit_library_infowidget inqiskit.tools.jupyteror theformattedkwarg on theqasm()method.The pulse
bufferoption found inqiskit.pulse.Channelandqiskit.pulse.Schedulewas deprecated in Terra 0.11.0 and has now been removed. To add a delay on a channel or in a schedule, specify it explicitly in your Schedule with a Delay:sched = Schedule() sched += Delay(5)(DriveChannel(0))
PulseChannelSpec, which was deprecated in Terra 0.11.0, has now been removed. Use BackendConfiguration instead:config = backend.configuration() drive_chan_0 = config.drives(0) acq_chan_0 = config.acquires(0)
or, simply reference the channel directly, such as
DriveChannel(index).An import path was deprecated in Terra 0.10.0 and has now been removed: for
PulseChannel,DriveChannel,MeasureChannel, andControlChannel, usefrom qiskit.pulse.channels import Xin place offrom qiskit.pulse.channels.pulse_channels import X.The pass
qiskit.transpiler.passes.CSPLayout(which was introduced in the 0.11.0 release) has been added to the preset pass manager for optimization levels 2 and 3. For level 2, there is a call limit of 1,000 and a timeout of 10 seconds. For level 3, the call limit is 10,000 and the timeout is 1 minute.Now that the pass is included in the preset pass managers the python-constraint package is not longer an optional dependency and has been added to the requirements list.
The
TranspileConfigclass which was previously used to set run time configuration for aqiskit.transpiler.PassManagerhas been removed and replaced by a new classqiskit.transpile.PassManagerConfig. This new class has been structured to include only the information needed to construct aPassManager. The attributes of this class are:initial_layoutbasis_gatescoupling_mapbackend_propertiesseed_transpiler
The function
transpile_circuitinqiskit.transpilerhas been removed. To transpile a circuit with a customPassManagernow you should use therun()method of the :class:~qiskit.transpiler.PassManager` object.The
QuantumCircuitmethoddraw()andqiskit.visualization.circuit_drawer()function will no longer include the initial state included in visualizations by default. If you would like to retain the initial state in the output visualization you need to set theinitial_statekwarg toTrue. For example, running:from qiskit import QuantumCircuit circuit = QuantumCircuit(2) circuit.measure_all() circuit.draw(output='text')
░ ┌─┐ q_0: ─░─┤M├─── ░ └╥┘┌─┐ q_1: ─░──╫─┤M├ ░ ║ └╥┘ meas: 2/════╩══╩═ 0 1This no longer includes the initial state. If you'd like to retain it you can run:
from qiskit import QuantumCircuit circuit = QuantumCircuit(2) circuit.measure_all() circuit.draw(output='text', initial_state=True)
░ ┌─┐ q_0: |0>─░─┤M├─── ░ └╥┘┌─┐ q_1: |0>─░──╫─┤M├ ░ ║ └╥┘ meas: 0 2/════╩══╩═ 0 1qiskit.compiler.transpile()(andqiskit.execute.execute(), which usestranspileinternally) will now raise an error when thepass_managerkwarg is set and a value is set for other kwargs that are already set in an instantiatedPassManagerobject. Previously, these conflicting kwargs would just be silently ignored and the values in thePassManagerinstance would be used. For example:from qiskit.circuit import QuantumCircuit from qiskit.transpiler.pass_manager_config import PassManagerConfig from qiskit.transpiler import preset_passmanagers from qiskit.compiler import transpile qc = QuantumCircuit(5) config = PassManagerConfig(basis_gates=['u3', 'cx']) pm = preset_passmanagers.level_0_pass_manager(config) transpile(qc, optimization_level=3, pass_manager=pm)
will now raise an error while prior to this release the value in
pmwould just silently be used and the value for theoptimization_levelkwarg would be ignored. Thetranspilekwargs this applies to are:optimization_levelbasis_gatescoupling_mapseed_transpilerbackend_propertiesinitial_layoutlayout_methodrouting_methodbackend
The
Operator,Clifford,SparsePauliOp,PauliTable,StabilizerTable, operator classes have an addedcallmethod that allows them to assign a qargs to the operator for use with thecompose(),dot(),evolve(),``+``, and-operations.The addition method of the
qiskit.quantum_info.Operator, class now accepts aqargkwarg to allow adding a smaller operator to a larger one assuming identities on the other subsystems (same as forqargsoncompose()anddot()methods). This allows subsystem addition using the call method as with composition. This support is added to all BaseOperator subclasses (ScalarOp,Operator,QuantumChannel).For example:
from qiskit.quantum_info import Operator, ScalarOp ZZ = Operator.from_label('ZZ') # Initialize empty Hamiltonian n_qubits = 10 ham = ScalarOp(2 ** n_qubits, coeff=0) # Add 2-body nearest neighbour terms for j in range(n_qubits - 1): ham = ham + ZZ([j, j+1])
The
BaseOperatorclass has been updated so that addition, subtraction and scalar multiplication are no longer abstract methods. This means that they are no longer required to be implemented in subclasses if they are not supported. The base class will raise aNotImplementedErrorwhen the methods are not defined.The
qiskit.quantum_info.random_density_matrix()function will now return a randomDensityMatrixobject. In previous releases it returned a numpy array.The
qiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixclasses no longer copy the input array if it is already the correct dtype.fastjsonschema is added as a dependency. This is used for much faster validation of qobj dictionaries against the JSON schema when the
to_dict()method is called on qobj objects with thevalidatekeyword argument set toTrue.The qobj construction classes in
qiskit.qobjwill no longer validate against the qobj jsonschema by default. These include the following classes:If you were relying on this validation or would like to validate them against the qobj schema this can be done by setting the
validatekwarg toTrueonto_dict()method from either of the top level Qobj classesQasmQobjorPulseQobj. For example:which will validate the output dictionary against the Qobj jsonschema.
The output dictionary from
qiskit.qobj.QasmQobj.to_dict()andqiskit.qobj.PulseQobj.to_dict()is no longer in a format for direct json serialization as expected by IBMQ's API. These Qobj objects are the current format we use for passing experiments to providers/backends and while having a dictionary format that could just be passed to the IBMQ API directly was moderately useful forqiskit-ibmq-provider, it made things more difficult for other providers. Especially for providers that wrap local simulators. Moving forward the definitions of what is passed between providers and the IBMQ API request format will be further decoupled (in a backwards compatible manner) which should ease the burden of writing providers and backends.In practice, the only functional difference between the output of these methods now and previous releases is that complex numbers are represented with the
complextype and numpy arrays are not silently converted to list anymore. If you were previously callingjson.dumps()directly on the output ofto_dict()after this release a custom json encoder will be needed to handle these cases. For example:import json from qiskit.circuit import ParameterExpression from qiskit import qobj my_qasm = qobj.QasmQobj( qobj_id='12345', header=qobj.QobjHeader(), config=qobj.QasmQobjConfig(shots=1024, memory_slots=2, max_credits=10), experiments=[ qobj.QasmQobjExperiment(instructions=[ qobj.QasmQobjInstruction(name='u1', qubits=[1], params=[0.4]), qobj.QasmQobjInstruction(name='u2', qubits=[1], params=[0.4, 0.2]) ]) ] ) qasm_dict = my_qasm.to_dict() class QobjEncoder(json.JSONEncoder): """A json encoder for pulse qobj""" def default(self, obj): # Convert numpy arrays: if hasattr(obj, 'tolist'): return obj.tolist() # Use Qobj complex json format: if isinstance(obj, complex): return (obj.real, obj.imag) if isinstance(obj, ParameterExpression): return float(obj) return json.JSONEncoder.default(self, obj) json_str = json.dumps(qasm_dict, cls=QobjEncoder)
will generate a json string in the same exact manner that
json.dumps(my_qasm.to_dict())did in previous releases.CmdDefhas been deprecated since Terra 0.11.0 and has been removed. Please continue to useInstructionScheduleMapinstead.The methods
cmdsandcmd_qubitsinInstructionScheduleMaphave been deprecated since Terra 0.11.0 and have been removed. Please useinstructionsandqubits_with_instructioninstead.PulseDefaults have reported
qubit_freq_estandmeas_freq_estin Hz rather than GHz since Terra release 0.11.0. A warning which notified of this change has been removed.The previously deprecated (in the 0.11.0 release) support for passsing in
qiskit.circuit.Instructionparameters of typessympy.Basic,sympy.Expr,qiskit.qasm.node.node.Node(QASM AST node) andsympy.Matrixhas been removed. The supported types for instruction parameters are:intfloatcomplexstrlistnp.ndarray
The following properties of
BackendConfiguration:dtdtmrep_time
all have units of seconds. Prior to release 0.11.0,
dtanddtmhad units of nanoseconds. Prior to release 0.12.0,rep_timehad units of microseconds. The warnings alerting users of these changes have now been removed fromBackendConfiguration.A new requirement has been added to the requirements list, retworkx. It is an Apache 2.0 licensed graph library that has a similar API to networkx and is being used to significantly speed up the
qiskit.dagcircuit.DAGCircuitoperations as part of the transpiler. There are binaries published on PyPI for all the platforms supported by Qiskit Terra but if you're using a platform where there aren't precompiled binaries published refer to the retworkx documentation for instructions on pip installing from sdist.If you encounter any issues with the transpiler or DAGCircuit class as part of the transition you can switch back to the previous networkx implementation by setting the environment variable
USE_RETWORKXtoN. This option will be removed in the 0.14.0 release.
Deprecation Notes¶
Passing in the data to the constructor for
qiskit.dagcircuit.DAGNodeas a dictionary argdata_dictis deprecated and will be removed in a future release. Instead you should now pass the fields in as kwargs to the constructor. For example the previous behavior of:from qiskit.dagcircuit import DAGNode data_dict = { 'type': 'in', 'name': 'q_0', } node = DAGNode(data_dict)
should now be:
from qiskit.dagcircuit import DAGNode node = DAGNode(type='in', name='q_0')
The naming of gate objects and methods have been updated to be more consistent. The following changes have been made:
The Pauli gates all have one uppercase letter only (
I,X,Y,Z)The parameterized Pauli gates (i.e. rotations) prepend the uppercase letter
R(RX,RY,RZ)A controlled version prepends the uppercase letter
C(CX,CRX,CCX)Gates are named according to their action, not their alternative names (
CCX, notToffoli)
The old names have been deprecated and will be removed in a future release. This is a list of the changes showing the old and new class, name attribute, and methods. If a new column is blank then there is no change for that.
Table 2 Gate Name Changes¶ Old Class
New Class
Old Name Attribute
New Name Attribute
Old
qiskit.circuit.QuantumCircuitmethodNew
qiskit.circuit.QuantumCircuitmethodToffoliGateCCXGateccxCrxGateCRXGatecrxCryGateCRYGatecryCrzGateCRZGatecrzFredkinGateCSwapGatecswapCu1GateCU1Gatecu1Cu3GateCU3Gatecu3CnotGateCXGatecxCyGateCYGatecyCzGateCZGateczDiagGateDiagonalGatediagdiagonaldiag_gateIdGateIGateididenIsometryisoisometryisometry()andiso()UCGUCGatemultiplexerucgUCRotUCPauliRotGateUCXUCRXGateucrotXucrxucxUCYUCRYGateucrotyucryucyUCZUCRZGateucrotzucrzuczThe kwarg
periodfor the functionsquare(),sawtooth(), andtriangle()inqiskit.pulse.pulse_libis now deprecated and will be removed in a future release. Instead you should now use thefreqkwarg to set the frequency.The
DAGCircuit.compose_back()andDAGCircuit.extend_back()methods are deprecated and will be removed in a future release. Instead you should use theqiskit.dagcircuit.DAGCircuit.compose()method, which is a more general and more flexible method that provides the same functionality.The
callbackkwarg of theqiskit.transpiler.PassManagerclass's constructor has been deprecated and will be removed in a future release. Instead of setting it at the object level during creation it should now be set as a kwarg parameter on theqiskit.transpiler.PassManager.run()method.The
n_qubitsandnumberofqubitskeywords are deprecated throughout Terra and replaced bynum_qubits. The old names will be removed in a future release. The objects affected by this change are listed below:Table 3 New Methods¶ Class
Old Method
New Method
n_qubitsnumberofqubitsTable 4 New arguments¶ Function
Old Argument
New Argument
n_qubitsnum_qubitsMSGaten_qubitnum_qubitsThe function
qiskit.quantum_info.synthesis.euler_angles_1qis now deprecated. It has been superseded by theqiskit.quantum_info.OneQubitEulerDecomposerclass which provides the same functionality through:OneQubitEulerDecomposer().angles(mat)
The
pass_managerkwarg for theqiskit.compiler.transpile()has been deprecated and will be removed in a future release. Moving forward the preferred way to transpile a circuit with a customPassManagerobject is to use therun()method of thePassManagerobject.The
qiskit.quantum_info.random_state()function has been deprecated and will be removed in a future release. Instead you should use theqiskit.quantum_info.random_statevector()function.The
add,subtract, andmultiplymethods of theqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixclasses are deprecated and will be removed in a future release. Instead you shoulde use+,-,*binary operators instead.Deprecates
qiskit.quantum_info.Statevector.to_counts(),qiskit.quantum_info.DensityMatrix.to_counts(), andqiskit.quantum_info.counts.state_to_counts(). These functions are superseded by the class methodsqiskit.quantum_info.Statevector.probabilities_dict()andqiskit.quantum_info.DensityMatrix.probabilities_dict().SamplePulseandParametricPulses (e.g.Gaussian) now subclass fromPulseand have been moved to theqiskit.pulse.pulse_lib. The previous path viapulse.commandsis deprecated and will be removed in a future release.DelayInstructionhas been deprecated and replaced byDelay. This new instruction has been taken over the previousCommandDelay. The migration pattern is:Delay(<duration>)(<channel>) -> Delay(<duration>, <channel>) DelayInstruction(Delay(<duration>), <channel>) -> Delay(<duration>, <channel>)
Until the deprecation period is over, the previous
Delaysyntax of calling a command on a channel will also be supported:Delay(<phase>)(<channel>)
The new
Delayinstruction does not support acommandattribute.FrameChangeandFrameChangeInstructionhave been deprecated and replaced byShiftPhase. The changes are:FrameChange(<phase>)(<channel>) -> ShiftPhase(<phase>, <channel>) FrameChangeInstruction(FrameChange(<phase>), <channel>) -> ShiftPhase(<phase>, <channel>)
Until the deprecation period is over, the previous FrameChange syntax of calling a command on a channel will be supported:
ShiftPhase(<phase>)(<channel>)
The
callmethod ofSamplePulseandParametricPulses have been deprecated. The migration is as follows:Pulse(<*args>)(<channel>) -> Play(Pulse(*args), <channel>)
AcquireInstructionhas been deprecated and replaced byAcquire. The changes are:Acquire(<duration>)(<**channels>) -> Acquire(<duration>, <**channels>) AcquireInstruction(Acquire(<duration>), <**channels>) -> Acquire(<duration>, <**channels>)
Until the deprecation period is over, the previous Acquire syntax of calling the command on a channel will be supported:
Acquire(<duration>)(<**channels>)
Bug Fixes¶
The
BarrierBeforeFinalMeasurementstranspiler pass, included in the preset transpiler levels when targeting a physical device, previously inserted a barrier across only measured qubits. In some cases, this allowed the transpiler to insert a swap after a measure operation, rendering the circuit invalid for current devices. The pass has been updated so that the inserted barrier will span all qubits on the device. Fixes #3937When extending a
QuantumCircuitinstance (extendee) with another circuit (extension), the circuit is taken via reference. If a circuit is extended with itself that leads to an infinite loop as extendee and extension are the same. This bug has been resolved by copying the extension if it is the same object as the extendee. Fixes #3811Fixes a case in
qiskit.result.Result.get_counts(), where the results for an expirement could not be referenced if the experiment was initialized as a Schedule without a name. Fixes #2753Previously, replacing
Parameterobjects in a circuit with new Parameter objects prior to decomposing a circuit would result in the substituted values not correctly being substituted into the decomposed gates. This has been resolved such that binding and decomposition may occur in any order.The matplotlib output backend for the
qiskit.visualization.circuit_drawer()function andqiskit.circuit.QuantumCircuit.draw()method drawer has been fixed to renderCU1Gategates correctly. Fixes #3684A bug in
qiskit.circuit.QuantumCircuit.from_qasm_str()andqiskit.circuit.QuantumCircuit.from_qasm_file()when loading QASM with custom gates defined has been fixed. Now, loading this QASM:OPENQASM 2.0; include "qelib1.inc"; gate rinv q {sdg q; h q; sdg q; h q; } qreg q[1]; rinv q[0];
is equivalent to the following circuit:
rinv_q = QuantumRegister(1, name='q') rinv_gate = QuantumCircuit(rinv_q, name='rinv') rinv_gate.sdg(rinv_q) rinv_gate.h(rinv_q) rinv_gate.sdg(rinv_q) rinv_gate.h(rinv_q) rinv = rinv_gate.to_instruction() qr = QuantumRegister(1, name='q') expected = QuantumCircuit(qr, name='circuit') expected.append(rinv, [qr[0]])
Fixes #1566
Allow quantum circuit Instructions to have list parameter values. This is used in Aer for expectation value snapshot parameters for example
params = [[1.0, 'I'], [1.0, 'X']]]for \(\langle I + X\rangle\).Previously, for circuits containing composite gates (those created via
qiskit.circuit.QuantumCircuit.to_gate()orqiskit.circuit.QuantumCircuit.to_instruction()or their corresponding converters), attempting to bind the circuit more than once would result in only the first bind value being applied to all circuits when transpiled. This has been resolved so that the values provided for subsequent binds are correctly respected.
Other Notes¶
The qasm and pulse qobj classes:
from
qiskit.qobjhave all been reimplemented without using the marsmallow library. These new implementations are designed to be drop-in replacement (except for as noted in the upgrade release notes) but specifics inherited from marshmallow may not work. Please file issues for any incompatibilities found.
Aer 0.5.0¶
Added¶
Add support for terra diagonal gate
Add support for parameterized qobj
Fixed¶
Added postfix for linux on Raspberry Pi
Handle numpy array inputs from qobj
Ignis 0.3.0¶
Added¶
API documentation
CNOT-Dihedral randomized benchmarking
Accreditation module for output accrediation of noisy devices
Pulse calibrations for single qubits
Pulse Discriminator
Entanglement verification circuits
Gateset tomography for single-qubit gate sets
Adds randomized benchmarking utility functions
calculate_1q_epg,calculate_2q_epgfunctions to calculate 1 and 2-qubit error per gate from error per CliffordAdds randomized benchmarking utility functions
calculate_1q_epc,calculate_2q_epcfor calculating 1 and 2-qubit error per Clifford from error per gate
Changed¶
Support integer labels for qubits in tomography
Support integer labels for measurement error mitigation
Deprecated¶
Deprecates
twoQ_clifford_errorfunction. Usecalculate_2q_epcinstead.Python 3.5 support in qiskit-ignis is deprecated. Support will be removed on the upstream python community's end of life date for the version, which is 09/13/2020.
Aqua 0.6.5¶
No Change
IBM Q Provider 0.6.0¶
No Change
Qiskit 0.17.0¶
Terra 0.12.0¶
No Change
Aer 0.4.1¶
No Change
Ignis 0.2.0¶
No Change
Aqua 0.6.5¶
No Change
IBM Q Provider 0.6.0¶
New Features¶
There are three new exceptions:
VisualizationError,VisualizationValueError, andVisualizationTypeError. These are now used in the visualization modules when an exception is raised.You can now set the logging level and specify a log file using the environment variables
QSIKIT_IBMQ_PROVIDER_LOG_LEVELandQISKIT_IBMQ_PROVIDER_LOG_FILE, respectively. Note that the name of the logger isqiskit.providers.ibmq.qiskit.providers.ibmq.job.IBMQJobnow has a new methodscheduling_mode()that returns the scheduling mode the job is in.IQX-related tutorials that used to be in
qiskit-iqx-tutorialsare now inqiskit-ibmq-provider.
Changed¶
qiskit.providers.ibmq.IBMQBackend.jobs()now accepts a new boolean parameterdescending, which can be used to indicate whether the jobs should be returned in descending or ascending order.qiskit.providers.ibmq.managed.IBMQJobManagernow looks at the job limit and waits for old jobs to finish before submitting new ones if the limit has been reached.qiskit.providers.ibmq.IBMQBackend.status()now raises aqiskit.providers.ibmq.IBMQBackendApiProtocolErrorexception if there was an issue with validating the status.
Qiskit 0.16.0¶
Terra 0.12.0¶
No Change
Aer 0.4.0¶
No Change
Ignis 0.2.0¶
No Change
Aqua 0.6.4¶
No Change
IBM Q Provider 0.5.0¶
New Features¶
Some of the visualization and Jupyter tools, including gate/error map and backend information, have been moved from
qiskit-terratoqiskit-ibmq-provider. They are now under theqiskit.providers.ibmq.jupyterandqiskit.providers.ibmq.visualization. In addition, you can now use%iqx_dashboardto get a dashboard that provides both job and backend information.
Changed¶
JSON schema validation is no longer run by default on Qobj objects passed to
qiskit.providers.ibmq.IBMQBackend.run(). This significantly speeds up the execution of the run() method. Qobj objects are still validated on the server side, and invalid Qobjs will continue to raise exceptions. To force local validation, setvalidate_qobj=Truewhen you invokerun().
Qiskit 0.15.0¶
Terra 0.12.0¶
Prelude¶
The 0.12.0 release includes several new features and bug fixes. The biggest change for this release is the addition of support for parametric pulses to OpenPulse. These are Pulse commands which take parameters rather than sample points to describe a pulse. 0.12.0 is also the first release to include support for Python 3.8. It also marks the beginning of the deprecation for Python 3.5 support, which will be removed when the upstream community stops supporting it.
New Features¶
The pass
qiskit.transpiler.passes.CSPLayoutwas extended with two new parameters:call_limitandtime_limit. These options allow limiting how long the pass will run. The optioncall_limitlimits the number of times that the recursive function in the backtracking solver may be called. Similarly,time_limitlimits how long (in seconds) the solver will be allowed to run. The defaults are1000calls and10seconds respectively.qiskit.pulse.Acquirecan now be applied to a single qubit. This makes pulse programming more consistent and easier to reason about, as now all operations apply to a single channel. For example:acquire = Acquire(duration=10) schedule = Schedule() schedule.insert(60, acquire(AcquireChannel(0), MemorySlot(0), RegisterSlot(0))) schedule.insert(60, acquire(AcquireChannel(1), MemorySlot(1), RegisterSlot(1)))
A new method
qiskit.transpiler.CouplingMap.draw()was added toqiskit.transpiler.CouplingMapto generate a graphviz image from the coupling map graph. For example:from qiskit.transpiler import CouplingMap coupling_map = CouplingMap( [[0, 1], [1, 0], [1, 2], [1, 3], [2, 1], [3, 1], [3, 4], [4, 3]]) coupling_map.draw()
Parametric pulses have been added to OpenPulse. These are pulse commands which are parameterized and understood by the backend. Arbitrary pulse shapes are still supported by the SamplePulse Command. The new supported pulse classes are:
qiskit.pulse.ConstantPulse
They can be used like any other Pulse command. An example:
from qiskit.pulse import (Schedule, Gaussian, Drag, ConstantPulse, GaussianSquare) sched = Schedule(name='parametric_demo') sched += Gaussian(duration=25, sigma=4, amp=0.5j)(DriveChannel(0)) sched += Drag(duration=25, amp=0.1, sigma=5, beta=4)(DriveChannel(1)) sched += ConstantPulse(duration=25, amp=0.3+0.1j)(DriveChannel(1)) sched += GaussianSquare(duration=1500, amp=0.2, sigma=8, width=140)(MeasureChannel(0)) << sched.duration
The resulting schedule will be similar to a SamplePulse schedule built using
qiskit.pulse.pulse_lib, however, waveform sampling will be performed by the backend. The methodqiskit.pulse.Schedule.draw()can still be used as usual. However, the command will be converted to aSamplePulsewith theqiskit.pulse.ParametricPulse.get_sample_pulse()method, so the pulse shown may not sample the continuous function the same way that the backend will.This feature can be used to construct Pulse programs for any backend, but the pulses will be converted to
SamplePulseobjects if the backend does not support parametric pulses. Backends which support them will have the following new attribute:backend.configuration().parametric_pulses: List[str] # e.g. ['gaussian', 'drag', 'constant']
Note that the backend does not need to support all of the parametric pulses defined in Qiskit.
When the backend supports parametric pulses, and the Pulse schedule is built with them, the assembled Qobj is significantly smaller. The size of a PulseQobj built entirely with parametric pulses is dependent only on the number of instructions, whereas the size of a PulseQobj built otherwise will grow with the duration of the instructions (since every sample must be specified with a value).
Added utility functions,
qiskit.scheduler.measure()andqiskit.scheduler.measure_all()to qiskit.scheduler module. These functions return aqiskit.pulse.Scheduleobject which measures qubits using OpenPulse. For example:from qiskit.scheduler import measure, measure_all measure_q0_schedule = measure(qubits=[0], backend=backend) measure_all_schedule = measure_all(backend) measure_custom_schedule = measure(qubits=[0], inst_map=backend.defaults().instruction_schedule_map, meas_map=[[0]], qubit_mem_slots={0: 1})
Pulse
qiskit.pulse.Scheduleobjects now have better representations that for simple schedules should be valid Python expressions.The
qiskit.circuit.QuantumCircuitmethodsqiskit.circuit.QuantumCircuit.measure_active(),qiskit.circuit.QuantumCircuit.measure_all(), andqiskit.circuit.QuantumCircuit.remove_final_measurements()now have an addition kwarginplace. Wheninplaceis set toFalsethe function will return a modified copy of the circuit. This is different from the default behavior which will modify the circuit object in-place and return nothing.Several new constructor methods were added to the
qiskit.transpiler.CouplingMapclass for building objects with basic qubit coupling graphs. The new constructor methods are:For example, to use the new constructors to get a coupling map of 5 qubits connected in a linear chain you can now run:
from qiskit.transpiler import CouplingMap coupling_map = CouplingMap.from_line(5) coupling_map.draw()
Introduced a new pass
qiskit.transpiler.passes.CrosstalkAdaptiveSchedule. This pass aims to reduce the impact of crosstalk noise on a program. It uses crosstalk characterization data from the backend to schedule gates. When a pair of gates has high crosstalk, they get serialized using a barrier. Naive serialization is harmful because it incurs decoherence errors. Hence, this pass uses a SMT optimization approach to compute a schedule which minimizes the impact of crosstalk as well as decoherence errors.The pass takes as input a circuit which is already transpiled onto the backend i.e., the circuit is expressed in terms of physical qubits and swap gates have been inserted and decomposed into CNOTs if required. Using this circuit and crosstalk characterization data, a Z3 optimization is used to construct a new scheduled circuit as output.
To use the pass on a circuit circ:
dag = circuit_to_dag(circ) pass_ = CrosstalkAdaptiveSchedule(backend_prop, crosstalk_prop) scheduled_dag = pass_.run(dag) scheduled_circ = dag_to_circuit(scheduled_dag)
backend_propis aqiskit.providers.models.BackendPropertiesobject for the target backend.crosstalk_propis a dict which specifies conditional error rates. For two gatesg1andg2,crosstalk_prop[g1][g2]specifies the conditional error rate ofg1wheng1andg2are executed simultaneously. A method for generatingcrosstalk_propwill be added in a future release of qiskit-ignis. Until then you'll either have to already know the crosstalk properties of your device, or manually write your own device characterization experiments.In the preset pass manager for optimization level 1,
qiskit.transpiler.preset_passmanagers.level_1_pass_manager()ifqiskit.transpiler.passes.TrivialLayoutlayout pass is not a perfect match for a particular circuit, thenqiskit.transpiler.passes.DenseLayoutlayout pass is used instead.Added a new abstract method
qiskit.quantum_info.Operator.dot()to the abstractBaseOperatorclass, so it is included for all implementations of that abstract class, includingqiskit.quantum_info.OperatorandQuantumChannel(e.g.,qiskit.quantum_info.Choi) objects. This method returns the right operator multiplicationa.dot(b)\(= a \cdot b\). This is equivalent to calling the operatorqiskit.quantum_info.Operator.compose()method with the kwargfrontset toTrue.Added
qiskit.quantum_info.average_gate_fidelity()andqiskit.quantum_info.gate_error()functions to theqiskit.quantum_infomodule for working withqiskit.quantum_info.OperatorandQuantumChannel(e.g.,qiskit.quantum_info.Choi) objects.Added the
qiskit.quantum_info.partial_trace()function to theqiskit.quantum_infothat works withqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixquantum state classes. For example:from qiskit.quantum_info.states import Statevector from qiskit.quantum_info.states import DensityMatrix from qiskit.quantum_info.states import partial_trace psi = Statevector.from_label('10+') partial_trace(psi, [0, 1]) rho = DensityMatrix.from_label('10+') partial_trace(rho, [0, 1])
When
qiskit.circuit.QuantumCircuit.draw()orqiskit.visualization.circuit_drawer()is called with thewith_layoutkwarg set True (the default) the output visualization will now display the physical qubits as integers to clearly distinguish them from the virtual qubits.For Example:
from qiskit import QuantumCircuit from qiskit import transpile from qiskit.test.mock import FakeVigo qc = QuantumCircuit(3) qc.h(0) qc.cx(0, 1) qc.cx(0, 2) transpiled_qc = transpile(qc, FakeVigo()) transpiled_qc.draw(output='mpl')
Added new state measure functions to the
qiskit.quantum_infomodule:qiskit.quantum_info.entropy(),qiskit.quantum_info.mutual_information(),qiskit.quantum_info.concurrence(), andqiskit.quantum_info.entanglement_of_formation(). These functions work with theqiskit.quantum_info.Statevectorandqiskit.quantum_info.DensityMatrixclasses.The decomposition methods for single-qubit gates in
qiskit.quantum_info.synthesis.one_qubit_decompose.OneQubitEulerDecomposerhave been expanded to now also include the'ZXZ'basis, characterized by three rotations about the Z,X,Z axis. This now means that a general 2x2 Operator can be decomposed into following bases:U3,U1X,ZYZ,ZXZ,XYX,ZXZ.
Known Issues¶
Running functions that use
qiskit.tools.parallel_map()(for exampleqiskit.execute.execute(),qiskit.compiler.transpile(), andqiskit.transpiler.PassManager.run()) may not work when called from a script running outside of aif __name__ == '__main__':block when using Python 3.8 on MacOS. Other environments are unaffected by this issue. This is due to changes in how parallel processes are launched by Python 3.8 on MacOS. IfRuntimeErrororAttributeErrorare raised by scripts that are directly callingparallel_map()or when calling a function that uses it internally with Python 3.8 on MacOS embedding the script calls insideif __name__ == '__main__':should workaround the issue. For example:from qiskit import QuantumCircuit, QiskitError from qiskit import execute, BasicAer qc1 = QuantumCircuit(2, 2) qc1.h(0) qc1.cx(0, 1) qc1.measure([0,1], [0,1]) # making another circuit: superpositions qc2 = QuantumCircuit(2, 2) qc2.h([0,1]) qc2.measure([0,1], [0,1]) execute([qc1, qc2], BasicAer.get_backend('qasm_simulator'))
should be changed to:
from qiskit import QuantumCircuit, QiskitError from qiskit import execute, BasicAer def main(): qc1 = QuantumCircuit(2, 2) qc1.h(0) qc1.cx(0, 1) qc1.measure([0,1], [0,1]) # making another circuit: superpositions qc2 = QuantumCircuit(2, 2) qc2.h([0,1]) qc2.measure([0,1], [0,1]) execute([qc1, qc2], BasicAer.get_backend('qasm_simulator')) if __name__ == '__main__': main()
if errors are encountered with Python 3.8 on MacOS.
Upgrade Notes¶
The value of the
rep_timeparameter for Pulse backend's configuration object is now in units of seconds, not microseconds. The first time aPulseBackendConfigurationobject is initialized it will raise a single warning to the user to indicate this.The
rep_timeargument forqiskit.compiler.assemble()now takes in a value in units of seconds, not microseconds. This was done to make the units with everything else in pulse. If you were passing in a value forrep_timeensure that you update the value to account for this change.The value of the
base_gateproperty ofqiskit.circuit.ControlledGateobjects has been changed from the class of the base gate to an instance of the class of the base gate.The
base_gate_nameproperty ofqiskit.circuit.ControlledGatehas been removed; you can get the name of the base gate by accessingbase_gate.nameon the object. For example:from qiskit import QuantumCircuit from qiskit.extensions import HGate qc = QuantumCircuit(3) cch_gate = HGate().control(2) base_gate_name = cch_gate.base_gate.name
Changed
qiskit.quantum_info.Operatormagic methods so that__mul__(which gets executed by python's multiplication operation, if the left hand side of the operation has it defined) implements right matrix multiplication (i.e.qiskit.quantum_info.Operator.dot()), and__rmul__(which gets executed by python's multiplication operation from the right hand side of the operation if the left does not have__mul__defined) implements scalar multiplication (i.e.qiskit.quantum_info.Operator.multiply()). Previously both methods implemented scalar multiplciation.The second argument of the
qiskit.quantum_info.process_fidelity()function,target, is now optional. If a target unitary is not specified, then process fidelity of the input channel with the identity operator will be returned.qiskit.compiler.assemble()will now respect the configuredmax_shotsvalue for a backend. If a value for theshotskwarg is specified that exceed the max shots set in the backend configuration the function will now raise aQiskitErrorexception. Additionally, if no shots argument is provided the default value is either 1024 (the previous behavior) ormax_shotsfrom the backend, whichever is lower.
Deprecation Notes¶
Methods for adding gates to a
qiskit.circuit.QuantumCircuitwith abbreviated keyword arguments (e.g.ctl,tgt) have had their keyword arguments renamed to be more descriptive (e.g.control_qubit,target_qubit). The old names have been deprecated. A table including the old and new calling signatures for theQuantumCircuitmethods is included below.Table 5 New signatures for QuantumCircuitgate methods¶Instruction Type
Former Signature
New Signature
qiskit.extensions.HGateqc.h(q)qc.h(qubit)qiskit.extensions.CHGateqc.ch(ctl, tgt)qc.ch((control_qubit, target_qubit))qiskit.extensions.IdGateqc.iden(q)qc.iden(qubit)qiskit.extensions.RGateqc.iden(q)qc.iden(qubit)qiskit.extensions.RGateqc.r(theta, phi, q)qc.r(theta, phi, qubit)qiskit.extensions.RXGateqc.rx(theta, q)qc.rx(theta, qubit)qiskit.extensions.CrxGateqc.crx(theta, ctl, tgt)qc.crx(theta, control_qubit, target_qubit)qiskit.extensions.RYGateqc.ry(theta, q)qc.ry(theta, qubit)qiskit.extensions.CryGateqc.cry(theta, ctl, tgt)qc.cry(theta, control_qubit, target_qubit)qiskit.extensions.RZGateqc.rz(phi, q)qc.rz(phi, qubit)qiskit.extensions.CrzGateqc.crz(theta, ctl, tgt)qc.crz(theta, control_qubit, target_qubit)qiskit.extensions.SGateqc.s(q)qc.s(qubit)qiskit.extensions.SdgGateqc.sdg(q)qc.sdg(qubit)qiskit.extensions.FredkinGateqc.cswap(ctl, tgt1, tgt2)qc.cswap(control_qubit, target_qubit1, target_qubit2)qiskit.extensions.TGateqc.t(q)qc.t(qubit)qiskit.extensions.TdgGateqc.tdg(q)qc.tdg(qubit)qiskit.extensions.U1Gateqc.u1(theta, q)qc.u1(theta, qubit)qiskit.extensions.Cu1Gateqc.cu1(theta, ctl, tgt)qc.cu1(theta, control_qubit, target_qubit)qiskit.extensions.U2Gateqc.u2(phi, lam, q)qc.u2(phi, lam, qubit)qiskit.extensions.U3Gateqc.u3(theta, phi, lam, q)qc.u3(theta, phi, lam, qubit)qiskit.extensions.Cu3Gateqc.cu3(theta, phi, lam, ctl, tgt)qc.cu3(theta, phi, lam, control_qubit, target_qubit)qiskit.extensions.XGateqc.x(q)qc.x(qubit)qiskit.extensions.CnotGateqc.cx(ctl, tgt)qc.cx(control_qubit, target_qubit)qiskit.extensions.ToffoliGateqc.ccx(ctl1, ctl2, tgt)qc.ccx(control_qubit1, control_qubit2, target_qubit)qiskit.extensions.YGateqc.y(q)qc.y(qubit)qiskit.extensions.CyGateqc.cy(ctl, tgt)qc.cy(control_qubit, target_qubit)qiskit.extensions.ZGateqc.z(q)qc.z(qubit)qiskit.extensions.CzGateqc.cz(ctl, tgt)qc.cz(control_qubit, target_qubit)Running
qiskit.pulse.Acquireon multiple qubits has been deprecated and will be removed in a future release. Additionally, theqiskit.pulse.AcquireInstructionparametersmem_slotsandreg_slotshave been deprecated. Insteadreg_slotandmem_slotshould be used instead.The attribute of the
qiskit.providers.models.PulseDefaultsclasscircuit_instruction_maphas been deprecated and will be removed in a future release. Instead you should use the new attributeinstruction_schedule_map. This was done to match the type of the value of the attribute, which is anInstructionScheduleMap.The
qiskit.pulse.PersistentValuecommand is deprecated and will be removed in a future release. Similar functionality can be achieved with theqiskit.pulse.ConstantPulsecommand (one of the new parametric pulses). Compare the following:from qiskit.pulse import Schedule, PersistentValue, ConstantPulse, \ DriveChannel # deprecated implementation sched_w_pv = Schedule() sched_w_pv += PersistentValue(value=0.5)(DriveChannel(0)) sched_w_pv += PersistentValue(value=0)(DriveChannel(0)) << 10 # preferred implementation sched_w_const = Schedule() sched_w_const += ConstantPulse(duration=10, amp=0.5)(DriveChannel(0))
Python 3.5 support in qiskit-terra is deprecated. Support will be removed in the first release after the upstream Python community's end of life date for the version, which is 09/13/2020.
The
require_cptpkwarg of theqiskit.quantum_info.process_fidelity()function has been deprecated and will be removed in a future release. It is superseded by two separate kwargsrequire_cpandrequire_tp.Setting the
scaleparameter forqiskit.circuit.QuantumCircuit.draw()andqiskit.visualization.circuit_drawer()as the first positional argument is deprecated and will be removed in a future release. Instead you should usescaleas keyword argument.The
qiskit.tools.qi.qimodule is deprecated and will be removed in a future release. The legacy functions in the module have all been superseded by functions and classes in theqiskit.quantum_infomodule. A table of the deprecated functions and their replacement are below:Table 6 qiskit.tools.qi.qireplacements¶Deprecated
Replacement
qiskit.tools.partial_trace()qiskit.tools.choi_to_pauli()qiskit.quantum_info.Choiandquantum_info.PTMqiskit.tools.chop()numpy.roundqiskit.tools.qi.qi.outernumpy.outerqiskit.tools.concurrence()qiskit.tools.shannon_entropy()qiskit.tools.entropy()qiskit.tools.mutual_information()qiskit.tools.entanglement_of_formation()qiskit.tools.is_pos_def()quantum_info.operators.predicates.is_positive_semidefinite_matrixThe
qiskit.quantum_info.states.statesmodule is deprecated and will be removed in a future release. The legacy functions in the module have all been superseded by functions and classes in theqiskit.quantum_infomodule.Table 7 qiskit.quantum_info.states.statesreplacements¶Deprecated
Replacement
qiskit.quantum_info.states.states.basis_stateqiskit.quantum_info.states.states.projectorThe
scalingparameter of thedraw()method for theScheduleandPulseobjects was deprecated and will be removed in a future release. Instead the newscaleparameter should be used. This was done to have a consistent argument between pulse and circuit drawings. For example:#The consistency in parameters is seen below #For circuits circuit = QuantumCircuit() circuit.draw(scale=0.2) #For pulses pulse = SamplePulse() pulse.draw(scale=0.2) #For schedules schedule = Schedule() schedule.draw(scale=0.2)
Bug Fixes¶
Previously, calling
qiskit.circuit.QuantumCircuit.bind_parameters()prior to decomposing a circuit would result in the bound values not being correctly substituted into the decomposed gates. This has been resolved such that binding and decomposition may occur in any order. Fixes issue #2482 and issue #3509The
Collect2qBlockspass had previously not considered classical conditions when determining whether to include a gate within an existing block. In some cases, this resulted in classical conditions being lost when transpiling withoptimization_level=3. This has been resolved so that classically conditioned gates are never included in a block. Fixes issue #3215All the output types for the circuit drawers in
qiskit.circuit.QuantumCircuit.draw()andqiskit.visualization.circuit_drawer()have fixed and/or improved support for drawing controlled custom gates. Fixes issue #3546, issue #3763, and issue #3764Explanation and examples have been added to documentation for the
qiskit.circuit.QuantumCircuitmethods for adding gates:qiskit.circuit.QuantumCircuit.ccx(),qiskit.circuit.QuantumCircuit.ch(),qiskit.circuit.QuantumCircuit.crz(),qiskit.circuit.QuantumCircuit.cswap(),qiskit.circuit.QuantumCircuit.cu1(),qiskit.circuit.QuantumCircuit.cu3(),qiskit.circuit.QuantumCircuit.cx(),qiskit.circuit.QuantumCircuit.cy(),qiskit.circuit.QuantumCircuit.cz(),qiskit.circuit.QuantumCircuit.h(),qiskit.circuit.QuantumCircuit.iden(),qiskit.circuit.QuantumCircuit.rx(),qiskit.circuit.QuantumCircuit.ry(),qiskit.circuit.QuantumCircuit.rz(),qiskit.circuit.QuantumCircuit.s(),qiskit.circuit.QuantumCircuit.sdg(),qiskit.circuit.QuantumCircuit.swap(),qiskit.circuit.QuantumCircuit.t(),qiskit.circuit.QuantumCircuit.tdg(),qiskit.circuit.QuantumCircuit.u1(),qiskit.circuit.QuantumCircuit.u2(),qiskit.circuit.QuantumCircuit.u3(),qiskit.circuit.QuantumCircuit.x(),qiskit.circuit.QuantumCircuit.y(),qiskit.circuit.QuantumCircuit.z(). Fixes issue #3400Fixes for handling of complex number parameter in circuit visualization. Fixes issue #3640
Other Notes¶
The transpiler passes in the
qiskit.transpiler.passesdirectory have been organized into subdirectories to better categorize them by functionality. They are still all accessible under theqiskit.transpiler.passesnamespace.
Aer 0.4.0¶
Added¶
Added
NoiseModel.from_backendfor building a basic device noise model for an IBMQ backend (#569)Added multi-GPU enabled simulation methods to the
QasmSimulator,StatevectorSimulator, andUnitarySimulator. The qasm simulator has gpu version of the density matrix and statevector methods and can be accessed using"method": "density_matrix_gpu"or"method": "statevector_gpu"inbackend_options. The statevector simulator gpu method can be accessed using"method": "statevector_gpu". The unitary simulator GPU method can be accessed using"method": "unitary_gpu". These backends use CUDA and require an NVidia GPU.(#544)Added
PulseSimulatorbackend (#542)Added
PulseSystemModelandHamiltonianModelclasses to represent models to be used inPulseSimulator(#496, #493)Added
duffing_model_generatorsto generatePulseSystemModelobjects from a list of parameters (#516)Migrated ODE function solver to C++ (#442, #350)
Added high level pulse simulator tests (#379)
CMake BLAS_LIB_PATH flag to set path to look for BLAS lib (#543)
Changed¶
Changed the structure of the
srcdirectory to organise simulator source code. Simulator controller headers were moved tosrc/controllersand simulator method State headers are insrc/simulators(#544)Moved the location of several functions (#568): * Moved contents of
qiskit.provider.aer.noise.errorsinto theqiskit.providers.noisemodule * Moved contents ofqiskit.provider.aer.noise.utilsinto theqiskit.provider.aer.utilsmodule.Enabled optimization to aggregate consecutive gates in a circuit (fusion) by default (#579).
Deprecated¶
Deprecated
utils.qobj_utilsfunctions (#568)Deprecated
qiskit.providers.aer.noise.device.basic_device_noise_model. It is superseded by theNoiseModel.from_backendmethod (#569)
Removed¶
Removed
NoiseModel.as_dict,QuantumError.as_dict,ReadoutError.as_dict, andQuantumError.kronmethods that were deprecated in 0.3 (#568).
Ignis 0.2¶
No Change
Aqua 0.6¶
No Change
IBM Q Provider 0.4.6¶
Added¶
Several new methods were added to
IBMQBackend:wait_for_final_state()blocks until the job finishes. It takes a callback function that it will invoke after every query to provide feedback.active_jobs()returns the jobs submitted to a backend that are currently in an unfinished status.job_limit()returns the job limit for a backend.remaining_jobs_count()returns the number of jobs that you can submit to the backend before job limit is reached.
QueueInfonow has a newformat()method that returns a formatted string of the queue information.IBMQJobnow has three new methods:done(),running(), andcancelled()that are used to indicate job status.qiskit.providers.ibmq.ibmqbackend.IBMQBackend.run()now accepts an optional job_tags parameter. If specified, the job_tags are assigned to the job, which can later be used as a filter inqiskit.providers.ibmq.ibmqbackend.IBMQBackend.jobs().IBMQJobManagernow has a new methodretrieve_job_set()that allows you to retrieve a previously submitted job set using the job set ID.
Changed¶
The
Exceptionhierarchy has been refined with more specialized classes. You can, however, continue to catch their parent exceptions (such asIBMQAccountError). Also, the exception classIBMQApiUrlErrorhas been replaced byIBMQAccountCredentialsInvalidUrlandIBMQAccountCredentialsInvalidToken.
Deprecated¶
The use of proxy urls without a protocol (e.g.
http://) is deprecated due to recent Python changes.
Qiskit 0.14.0¶
Terra 0.11.0¶
Prelude¶
The 0.11.0 release includes several new features and bug fixes. The biggest
change for this release is the addition of the pulse scheduler. This allows
users to define their quantum program as a QuantumCircuit and then map it
to the underlying pulse instructions that will control the quantum hardware to
implement the circuit.
New Features¶
Added 5 new commands to easily retrieve user-specific data from
BackendProperties:gate_property,gate_error,gate_length,qubit_property,t1,t2,readout_errorandfrequency. They return the specific values of backend properties. For example:from qiskit.test.mock import FakeOurense backend = FakeOurense() properties = backend.properties() gate_property = properties.gate_property('u1') gate_error = properties.gate_error('u1', 0) gate_length = properties.gate_length('u1', 0) qubit_0_property = properties.qubit_property(0) t1_time_0 = properties.t1(0) t2_time_0 = properties.t2(0) readout_error_0 = properties.readout_error(0) frequency_0 = properties.frequency(0)
Added method
Instruction.is_parameterized()to check if an instruction object is parameterized. This method returnsTrueif and only if instruction has aParameterExpressionorParameterobject for one of its params.Added a new analysis pass
Layout2qDistance. This pass allows to "score" a layout selection, onceproperty_set['layout']is set. The score will be the sum of distances for each two-qubit gate in the circuit, when they are not directly connected. This scoring does not consider direction in the coupling map. The lower the number, the better the layout selection is.For example, consider a linear coupling map
[0]--[2]--[1]and the following circuit:qr = QuantumRegister(2, 'qr') circuit = QuantumCircuit(qr) circuit.cx(qr[0], qr[1])
If the layout is
{qr[0]:0, qr[1]:1},Layout2qDistancewill setproperty_set['layout_score'] = 1. If the layout is{qr[0]:0, qr[1]:2}, then the result isproperty_set['layout_score'] = 0. The lower the score, the better.Added
qiskit.QuantumCircuit.cnotas an alias for thecxmethod ofQuantumCircuit. The namescnotandcxare often used interchangeably now the cx method can be called with either name.Added
qiskit.QuantumCircuit.toffolias an alias for theccxmethod ofQuantumCircuit. The namestoffoliandccxare often used interchangeably now the ccx method can be called with either name.Added
qiskit.QuantumCircuit.fredkinas an alias for thecswapmethod ofQuantumCircuit. The namesfredkinandcswapare often used interchangeably now the cswap method can be called with either name.The
latexoutput mode forqiskit.visualization.circuit_drawer()and theqiskit.circuit.QuantumCircuit.draw()method now has a mode to passthrough raw latex from gate labels and parameters. The syntax for doing this mirrors matplotlib's mathtext mode syntax. Any portion of a label string between a pair of '$' characters will be treated as raw latex and passed directly into the generated output latex. This can be leveraged to add more advanced formatting to circuit diagrams generated with the latex drawer.Prior to this release all gate labels were run through a utf8 -> latex conversion to make sure that the output latex would compile the string as expected. This is still what happens for all portions of a label outside the '$' pair. Also if you want to use a dollar sign in your label make sure you escape it in the label string (ie
'\$').You can mix and match this passthrough with the utf8 -> latex conversion to create the exact label you want, for example:
from qiskit import circuit circ = circuit.QuantumCircuit(2) circ.h([0, 1]) circ.append(circuit.Gate(name='α_gate', num_qubits=1, params=[0]), [0]) circ.append(circuit.Gate(name='α_gate$_2$', num_qubits=1, params=[0]), [1]) circ.append(circuit.Gate(name='\$α\$_gate', num_qubits=1, params=[0]), [1]) circ.draw(output='latex')
will now render the first custom gate's label as
α_gate, the second will beα_gatewith a 2 subscript, and the last custom gate's label will be$α$_gate.Add
ControlledGateclass for representing controlled gates. Controlled gate instances are created with thecontrol(n)method ofGateobjects wherenrepresents the number of controls. The control qubits come before the controlled qubits in the new gate. For example:from qiskit import QuantumCircuit from qiskit.extensions import HGate hgate = HGate() circ = QuantumCircuit(4) circ.append(hgate.control(3), [0, 1, 2, 3]) print(circ)
generates:
q_0: |0>──■── │ q_1: |0>──■── │ q_2: |0>──■── ┌─┴─┐ q_3: |0>┤ H ├ └───┘Allowed values of
meas_levelparameters and fields can now be a member from the IntEnum classqiskit.qobj.utils.MeasLevel. This can be used when callingexecute(or anywhere elsemeas_levelis specified) with a pulse experiment. For example:from qiskit import QuantumCircuit, transpile, schedule, execute from qiskit.test.mock import FakeOpenPulse2Q from qiskit.qobj.utils import MeasLevel, MeasReturnType backend = FakeOpenPulse2Q() qc = QuantumCircuit(2, 2) qc.h(0) qc.cx(0,1) qc_transpiled = transpile(qc, backend) sched = schedule(qc_transpiled, backend) execute(sched, backend, meas_level=MeasLevel.CLASSIFIED)
In this above example,
meas_level=MeasLevel.CLASSIFIEDandmeas_level=2can be used interchangably now.A new layout selector based on constraint solving is included. CSPLayout models the problem of finding a layout as a constraint problem and uses recursive backtracking to solve it.
cmap16 = CouplingMap(FakeRueschlikon().configuration().coupling_map) qr = QuantumRegister(5, 'q') circuit = QuantumCircuit(qr) circuit.cx(qr[0], qr[1]) circuit.cx(qr[0], qr[2]) circuit.cx(qr[0], qr[3]) pm = PassManager(CSPLayout(cmap16)) circuit_after = pm.run(circuit) print(pm.property_set['layout'])
Layout({ 1: Qubit(QuantumRegister(5, 'q'), 1), 2: Qubit(QuantumRegister(5, 'q'), 0), 3: Qubit(QuantumRegister(5, 'q'), 3), 4: Qubit(QuantumRegister(5, 'q'), 4), 15: Qubit(QuantumRegister(5, 'q'), 2) })
The parameter
CSPLayout(...,strict_direction=True)is more restrictive but it will guarantee there is no need of runningCXDirectionafter.pm = PassManager(CSPLayout(cmap16, strict_direction=True)) circuit_after = pm.run(circuit) print(pm.property_set['layout'])
Layout({ 8: Qubit(QuantumRegister(5, 'q'), 4), 11: Qubit(QuantumRegister(5, 'q'), 3), 5: Qubit(QuantumRegister(5, 'q'), 1), 6: Qubit(QuantumRegister(5, 'q'), 0), 7: Qubit(QuantumRegister(5, 'q'), 2) })
If the constraint system is not solvable, the layout property is not set.
circuit.cx(qr[0], qr[4]) pm = PassManager(CSPLayout(cmap16)) circuit_after = pm.run(circuit) print(pm.property_set['layout'])
NonePulseBackendConfiguration (accessed normally as backend.configuration()) has been extended with useful methods to explore its data and the functionality that exists in PulseChannelSpec. PulseChannelSpec will be deprecated in the future. For example:
backend = provider.get_backend(backend_name) config = backend.configuration() q0_drive = config.drive(0) # or, DriveChannel(0) q0_meas = config.measure(0) # MeasureChannel(0) q0_acquire = config.acquire(0) # AcquireChannel(0) config.hamiltonian # Returns a dictionary with hamiltonian info config.sample_rate() # New method which returns 1 / dt
PulseDefaults(accessed normally asbackend.defaults()) has an attribute,circuit_instruction_mapwhich has the methods of CmdDef. The new circuit_instruction_map is anInstructionScheduleMapobject with three new functions beyond what CmdDef had:qubit_instructions(qubits) returns the operations defined for the qubits
assert_has(instruction, qubits) raises an error if the op isn't defined
remove(instruction, qubits) like pop, but doesn't require parameters
There are some differences from the CmdDef:
__init__takes no argumentscmdsandcmd_qubitsare deprecated and replaced withinstructionsandqubits_with_instruction
Example:
backend = provider.get_backend(backend_name) inst_map = backend.defaults().circuit_instruction_map qubit = inst_map.qubits_with_instruction('u3')[0] x_gate = inst_map.get('u3', qubit, P0=np.pi, P1=0, P2=np.pi) pulse_schedule = x_gate(DriveChannel(qubit))
A new kwarg parameter,
show_framechange_channelsto optionally disable displaying channels with only framechange instructions in pulse visualizations was added to theqiskit.visualization.pulse_drawer()function andqiskit.pulse.Schedule.draw()method. When this new kwarg is set toFalsethe output pulse schedule visualization will not include any channels that only include frame changes.For example:
from qiskit.pulse import * from qiskit.pulse import library as pulse_lib gp0 = pulse_lib.gaussian(duration=20, amp=1.0, sigma=1.0) sched = Schedule() channel_a = DriveChannel(0) channel_b = DriveChannel(1) sched += Play(gp0, channel_a) sched = sched.insert(60, ShiftPhase(-1.57, channel_a)) sched = sched.insert(30, ShiftPhase(-1.50, channel_b)) sched = sched.insert(70, ShiftPhase(1.50, channel_b)) sched.draw(show_framechange_channels=False)
A new utility function
qiskit.result.marginal_counts()is added which allows marginalization of the counts over some indices of interest. This is useful when more qubits are measured than needed, and one wishes to get the observation counts for some subset of them only.When
passmanager.run(...)is invoked with more than one circuit, the transpilation of these circuits will run in parallel.PassManagers can now be sliced to create a new PassManager containing a subset of passes using the square bracket operator. This allow running or drawing a portion of the PassManager for easier testing and visualization. For example let's try to draw the first 3 passes of a PassManager pm, or run just the second pass on our circuit:
pm[0:4].draw() circuit2 = pm[1].run(circuit)
Also now, PassManagers can be created by adding two PassManagers or by directly adding a pass/list of passes to a PassManager.
pm = pm1[0] + pm2[1:3] pm += [setLayout, unroller]
A basic
schedulermodule has now been added to Qiskit. The scheduler schedules an input transpiledQuantumCircuitinto a pulseSchedule. The scheduler accepts as input aScheduleand either a pulseBackend, or aCmdDefwhich relates circuitInstructionobjects on specific qubits to pulse Schedules and ameas_mapwhich determines which measurements must occur together.Scheduling example:
from qiskit import QuantumCircuit, transpile, schedule from qiskit.test.mock import FakeOpenPulse2Q backend = FakeOpenPulse2Q() qc = QuantumCircuit(2, 2) qc.h(0) qc.cx(0,1) qc_transpiled = transpile(qc, backend) schedule(qc_transpiled, backend)
The scheduler currently supports two scheduling policies, as_late_as_possible (
alap) and as_soon_as_possible (asap), which respectively schedule pulse instructions to occur as late as possible or as soon as possible across qubits in a circuit. The scheduling policy may be selected with the input argumentmethod, for example:schedule(qc_transpiled, backend, method='alap')
It is easy to use a pulse
Schedulewithin aQuantumCircuitby mapping it to a custom circuit instruction such as a gate which may be used in aQuantumCircuit. To do this, first, define the custom gate and then add an entry into theCmdDeffor the gate, for each qubit that the gate will be applied to. The gate can then be used in theQuantumCircuit. At scheduling time the gate will be mapped to the underlying pulse schedule. Using this technique allows easy integration with preexisting qiskit modules such as Ignis.For example:
from qiskit import pulse, circuit, schedule from qiskit.pulse import pulse_lib custom_cmd_def = pulse.CmdDef() # create custom gate custom_gate = circuit.Gate(name='custom_gate', num_qubits=1, params=[]) # define schedule for custom gate custom_schedule = pulse.Schedule() custom_schedule += pulse_lib.gaussian(20, 1.0, 10)(pulse.DriveChannel) # add schedule to custom gate with same name custom_cmd_def.add('custom_gate', (0,), custom_schedule) # use custom gate in a circuit custom_qc = circuit.QuantumCircuit(1) custom_qc.append(custom_gate, qargs=[0]) # schedule the custom gate schedule(custom_qc, cmd_def=custom_cmd_def, meas_map=[[0]])
Known Issues¶
The feature for transpiling in parallel when
passmanager.run(...)is invoked with more than one circuit is not supported under Windows. See #2988 for more details.
Upgrade Notes¶
The
qiskit.pulse.channels.SystemTopologyclass was used as a helper class forPulseChannelSpec. It has been removed since with the deprecation ofPulseChannelSpecand changes toBackendConfigurationmake it unnecessary.The previously deprecated representation of qubits and classical bits as tuple, which was deprecated in the 0.9 release, has been removed. The use of
QubitandClbitobjects is the new way to represent qubits and classical bits.The previously deprecated representation of the basis set as single string has been removed. A list of strings is the new preferred way.
The method
BaseModel.as_dict, which was deprecated in the 0.9 release, has been removed in favor of the methodBaseModel.to_dict.In PulseDefaults (accessed normally as backend.defaults()),
qubit_freq_estandmeas_freq_estare now returned in Hz rather than GHz. This means the new return values are 1e9 * their previous value.dill was added as a requirement. This is needed to enable running
passmanager.run()in parallel for more than one circuit.The previously deprecated gate
UBase, which was deprecated in the 0.9 release, has been removed. The gateU3Gateshould be used instead.The previously deprecated gate
CXBase, which was deprecated in the 0.9 release, has been removed. The gateCnotGateshould be used instead.The instruction
snapshotused to implicitly convert thelabelparameter to string. That conversion has been removed and an error is raised if a string is not provided.The previously deprecated gate
U0Gate, which was deprecated in the 0.9 release, has been removed. The gateIdGateshould be used instead to insert delays.
Deprecation Notes¶
The
qiskit.pulse.CmdDefclass has been deprecated. Instead you should use theqiskit.pulse.InstructionScheduleMap. TheInstructionScheduleMapobject for a pulse enabled system can be accessed atbackend.defaults().instruction_schedules.PulseChannelSpecis being deprecated. UseBackendConfigurationinstead. The backend configuration is accessed normally asbackend.configuration(). The config has been extended with most of the functionality of PulseChannelSpec, with some modifications as follows, where 0 is an exemplary qubit index:pulse_spec.drives[0] -> config.drive(0) pulse_spec.measures[0] -> config.measure(0) pulse_spec.acquires[0] -> config.acquire(0) pulse_spec.controls[0] -> config.control(0)
Now, if there is an attempt to get a channel for a qubit which does not exist for the device, a
BackendConfigurationErrorwill be raised with a helpful explanation.The methods
memoryslotsandregisterslotsof the PulseChannelSpec have not been migrated to the backend configuration. These classical resources are not restrained by the physical configuration of a backend system. Please instantiate them directly:pulse_spec.memoryslots[0] -> MemorySlot(0) pulse_spec.registerslots[0] -> RegisterSlot(0)
The
qubitsmethod is not migrated to backend configuration. The result ofqubitscan be built as such:[q for q in range(backend.configuration().n_qubits)]
Qubitwithinpulse.channelshas been deprecated. They should not be used. It is possible to obtain channel <=> qubit mappings through the BackendConfiguration (or backend.configuration()).The function
qiskit.visualization.circuit_drawer.qx_color_scheme()has been deprecated. This function is no longer used internally and doesn't reflect the current IBM QX style. If you were using this function to generate a style dict locally you must save the output from it and use that dictionary directly.The Exception
TranspilerAccessErrorhas been deprecated. An alternative functionTranspilerErrorcan be used instead to provide the same functionality. This alternative function provides the exact same functionality but with greater generality.Buffers in Pulse are deprecated. If a nonzero buffer is supplied, a warning will be issued with a reminder to use a Delay instead. Other options would include adding samples to a pulse instruction which are (0.+0.j) or setting the start time of the next pulse to
schedule.duration + buffer.Passing in
sympy.Basic,sympy.Exprandsympy.Matrixtypes as instruction parameters are deprecated and will be removed in a future release. You'll need to convert the input to one of the supported types which are:intfloatcomplexstrnp.ndarray
Bug Fixes¶
The Collect2qBlocks and CommutationAnalysis passes in the transpiler had been unable to process circuits containing Parameterized gates, preventing Parameterized circuits from being transpiled at optimization_level 2 or above. These passes have been corrected to treat Parameterized gates as opaque.
The align_measures function had an issue where Measure stimulus pulses weren't properly aligned with Acquire pulses, resulting in an error. This has been fixed.
Uses of
numpy.random.seedhave been removed so that calls of qiskit functions do not affect results of future calls tonumpy.randomFixed race condition occurring in the job monitor when
job.queue_position()returnsNone.Noneis a valid return fromjob.queue_position().Backend support for
memory=Truenow checked when that kwarg is passed.QiskitErrorresults if not supported.When transpiling without a coupling map, there were no check in the amount of qubits of the circuit to transpile. Now the transpile process checks that the backend has enough qubits to allocate the circuit.
Other Notes¶
The
qiskit.result.marginal_counts()function replaces a similar utility function in qiskit-ignisqiskit.ignis.verification.tomography.marginal_counts(), which will be deprecated in a future qiskit-ignis release.All sympy parameter output type support have been been removed (or deprecated as noted) from qiskit-terra. This includes sympy type parameters in
QuantumCircuitobjects, qasm ast nodes, orQobjobjects.
Aer 0.3¶
No Change
Ignis 0.2¶
No Change
Aqua 0.6¶
No Change
IBM Q Provider 0.4¶
Prelude¶
The 0.4.0 release is the first release that makes use of all the features
of the new IBM Q API. In particular, the IBMQJob class has been revamped in
order to be able to retrieve more information from IBM Q, and a Job Manager
class has been added for allowing a higher-level and more seamless usage of
large or complex jobs. If you have not upgraded from the legacy IBM Q
Experience or QConsole yet, please ensure to revisit the release notes for
IBM Q Provider 0.3 (Qiskit 0.11) for more details on how to make the
transition. The legacy accounts will no longer be supported as of this release.
New Features¶
Job modifications¶
The IBMQJob class has been revised, and now mimics more closely to the
contents of a remote job along with new features:
You can now assign a name to a job, by specifying
IBMQBackend.run(..., job_name='...')when submitting a job. This name can be retrieved viaIBMQJob.name()and can be used for filtering.Jobs can now be shared with other users at different levels (global, per hub, group or project) via an optional
job_share_levelparameter when submitting the job.IBMQJobinstances now have more attributes, reflecting the contents of the remote IBM Q jobs. This implies that new attributes introduced by the IBM Q API will automatically and immediately be available for use (for example,job.new_api_attribute). The new attributes will be promoted to methods when they are considered stable (for example,job.name())..error_message()returns more information on why a job failed..queue_position()accepts arefreshparameter for forcing an update..result()accepts an optionalpartialparameter, for returning partial results, if any, of jobs that failed. Be aware thatResultmethods, such asget_counts()will raise an exception if applied on experiments that failed.
Please note that the changes include some low-level modifications of the class. If you were creating the instances manually, note that:
the signature of the constructor has changed to account for the new features.
the
.submit()method can no longer be called directly, and jobs are expected to be submitted either via the synchronousIBMQBackend.run()or via the Job Manager.
Job Manager¶
A new Job Manager (IBMQJobManager) has been introduced, as a higher-level
mechanism for handling jobs composed of multiple circuits or pulse schedules.
The Job Manager aims to provide a transparent interface, intelligently splitting
the input into efficient units of work and taking full advantage of the
different components. It will be expanded on upcoming versions, and become the
recommended entry point for job submission.
Its .run() method receives a list of circuits or pulse schedules, and
returns a ManagedJobSet instance, which can then be used to track the
statuses and results of these jobs. For example:
from qiskit.providers.ibmq.managed import IBMQJobManager
from qiskit.circuit.random import random_circuit
from qiskit import IBMQ
from qiskit.compiler import transpile
provider = IBMQ.load_account()
backend = provider.backends.ibmq_ourense
circs = []
for _ in range(1000000):
circs.append(random_circuit(2, 2))
transpile(circs, backend=backend)
# Farm out the jobs.
jm = IBMQJobManager()
job_set = jm.run(circs, backend=backend, name='foo')
job_set.statuses() # Gives a list of job statuses
job_set.report() # Prints detailed job information
results = job_set.results()
counts = results.get_counts(5) # Returns data for experiment 5
provider.backends modifications¶
The provider.backends member, which was previously a function that returned
a list of backends, has been promoted to a service. This implies that it can
be used both in the previous way, as a .backends() method, and also as a
.backends attribute with expanded capabilities:
it contains the existing backends from that provider as attributes, which can be used for autocompletion. For example:
my_backend = provider.get_backend('ibmq_qasm_simulator')
is equivalent to:
my_backend = provider.backends.ibmq_qasm_simulator
the
provider.backends.jobs()andprovider.backends.retrieve_job()methods can be used for retrieving provider-wide jobs.
Other changes¶
The
backend.properties()function now accepts an optionaldatetimeparameter. If specified, the function returns the backend properties closest to, but older than, the specified datetime filter.Some
warningshave been toned down tologger.warningmessages.
Qiskit 0.13.0¶
Terra 0.10.0¶
Prelude¶
The 0.10.0 release includes several new features and bug fixes. The biggest change for this release is the addition of initial support for using Qiskit with trapped ion trap backends.
New Features¶
Introduced new methods in
QuantumCircuitwhich allows the seamless adding or removing of measurements at the end of a circuit.measure_all()Adds a
barrierfollowed by ameasureoperation to all qubits in the circuit. Creates aClassicalRegisterof size equal to the number of qubits in the circuit, which store the measurements.measure_active()Adds a
barrierfollowed by ameasureoperation to all active qubits in the circuit. A qubit is active if it has at least one other operation acting upon it. Creates aClassicalRegisterof size equal to the number of active qubits in the circuit, which store the measurements.remove_final_measurements()Removes all final measurements and preceeding
barrierfrom a circuit. A measurement is considered "final" if it is not followed by any other operation, excluding barriers and other measurements. After the measurements are removed, if all of the classical bits in theClassicalRegisterare idle (have no operations attached to them), then theClassicalRegisteris removed.
Examples:
# Using measure_all() circuit = QuantumCircuit(2) circuit.h(0) circuit.measure_all() circuit.draw() # A ClassicalRegister with prefix measure was created. # It has 2 clbits because there are 2 qubits to measure ┌───┐ ░ ┌─┐ q_0: |0>┤ H ├─░─┤M├─── └───┘ ░ └╥┘┌─┐ q_1: |0>──────░──╫─┤M├ ░ ║ └╥┘ measure_0: 0 ═════════╩══╬═ ║ measure_1: 0 ════════════╩═ # Using measure_active() circuit = QuantumCircuit(2) circuit.h(0) circuit.measure_active() circuit.draw() # This ClassicalRegister only has 1 clbit because only 1 qubit is active ┌───┐ ░ ┌─┐ q_0: |0>┤ H ├─░─┤M├ └───┘ ░ └╥┘ q_1: |0>──────░──╫─ ░ ║ measure_0: 0 ═════════╩═ # Using remove_final_measurements() # Assuming circuit_all and circuit_active are the circuits from the measure_all and # measure_active examples above respectively circuit_all.remove_final_measurements() circuit_all.draw() # The ClassicalRegister is removed because, after the measurements were removed, # all of its clbits were idle ┌───┐ q_0: |0>┤ H ├ └───┘ q_1: |0>───── circuit_active.remove_final_measurements() circuit_active.draw() # This will result in the same circuit ┌───┐ q_0: |0>┤ H ├ └───┘ q_1: |0>─────Initial support for executing experiments on ion trap backends has been added.
An Rxx gate (rxx) and a global Mølmer–Sørensen gate (ms) have been added to the standard gate set.
A Cnot to Rxx/Rx/Ry decomposer
cnot_rxx_decomposeand a single qubit Euler angle decomposerOneQubitEulerDecomposerhave been added to thequantum_info.synthesismodule.A transpiler pass
MSBasisDecomposerhas been added to unroll circuits defined over U3 and Cnot gates into a circuit defined over Rxx,Ry and Rx. This pass will be included in preset pass managers for backends which include the 'rxx' gate in their supported basis gates.The backends in
qiskit.test.mocknow contain a snapshot of real device calibration data. This is accessible via theproperties()method for each backend. This can be used to test any code that depends on backend properties, such as noise-adaptive transpiler passes or device noise models for simulation. This will create a faster testing and development cycle without the need to go to live backends.Allows the Result class to return partial results. If a valid result schema is loaded that contains some experiments which succeeded and some which failed, this allows accessing the data from experiments that succeeded, while raising an exception for experiments that failed and displaying the appropriate error message for the failed results.
An
axkwarg has been added to the following visualization functions:qiskit.visualization.plot_histogramqiskit.visualization.plot_state_paulivecqiskit.visualization.plot_state_qsphereqiskit.visualization.circuit_drawer(mplbackend only)qiskit.QuantumCircuit.draw(mplbackend only)
This kwarg is used to pass in a
matplotlib.axes.Axesobject to the visualization functions. This enables integrating these visualization functions into a larger visualization workflow. Also, if an ax kwarg is specified then there is no return from the visualization functions.An
ax_realandax_imagkwarg has been added to the following visualization functions:qiskit.visualization.plot_state_hintonqiskit.visualization.plot_state_city
These new kargs work the same as the newly added
axkwargs for other visualization functions. However because these plots use two axes (one for the real component, the other for the imaginary component). Having two kwargs also provides the flexibility to only generate a visualization for one of the components instead of always doing both. For example:from matplotlib import pyplot as plt from qiskit.visualization import plot_state_hinton ax = plt.gca() plot_state_hinton(psi, ax_real=ax)
will only generate a plot of the real component.
A given pass manager now can be edited with the new method replace. This method allows to replace a particular stage in a pass manager, which can be handy when dealing with preset pass managers. For example, let's edit the layout selector of the pass manager used at optimization level 0:
from qiskit.transpiler.preset_passmanagers.level0 import level_0_pass_manager from qiskit.transpiler.transpile_config import TranspileConfig pass_manager = level_0_pass_manager(TranspileConfig(coupling_map=CouplingMap([[0,1]]))) pass_manager.draw()
[0] FlowLinear: SetLayout [1] Conditional: TrivialLayout [2] FlowLinear: FullAncillaAllocation, EnlargeWithAncilla, ApplyLayout [3] FlowLinear: Unroller
The layout selection is set in the stage [1]. Let's replace it with DenseLayout:
from qiskit.transpiler.passes import DenseLayout pass_manager.replace(1, DenseLayout(coupling_map), condition=lambda property_set: not property_set['layout']) pass_manager.draw()
[0] FlowLinear: SetLayout [1] Conditional: DenseLayout [2] FlowLinear: FullAncillaAllocation, EnlargeWithAncilla, ApplyLayout [3] FlowLinear: Unroller
If you want to replace it without any condition, you can use set-item shortcut:
pass_manager[1] = DenseLayout(coupling_map) pass_manager.draw()
[0] FlowLinear: SetLayout [1] FlowLinear: DenseLayout [2] FlowLinear: FullAncillaAllocation, EnlargeWithAncilla, ApplyLayout [3] FlowLinear: Unroller
Introduced a new pulse command
Delaywhich may be inserted into a pulseSchedule. This command accepts adurationand may be added to anyChannel. Other commands may not be scheduled on a channel during a delay.The delay can be added just like any other pulse command. For example:
from qiskit import pulse from qiskit.pulse.utils import pad dc0 = pulse.DriveChannel(0) delay = pulse.Delay(1) test_pulse = pulse.SamplePulse([1.0]) sched = pulse.Schedule() sched += test_pulse(dc0).shift(1) # build padded schedule by hand ref_sched = delay(dc0) | sched # pad schedule padded_sched = pad(sched) assert padded_sched == ref_sched
One may also pass additional channels to be padded and a time to pad until, for example:
from qiskit import pulse from qiskit.pulse.utils import pad dc0 = pulse.DriveChannel(0) dc1 = pulse.DriveChannel(1) delay = pulse.Delay(1) test_pulse = pulse.SamplePulse([1.0]) sched = pulse.Schedule() sched += test_pulse(dc0).shift(1) # build padded schedule by hand ref_sched = delay(dc0) | delay(dc1) | sched # pad schedule across both channels until up until the first time step padded_sched = pad(sched, channels=[dc0, dc1], until=1) assert padded_sched == ref_sched
Upgrade Notes¶
Assignments and modifications to the
dataattribute ofqiskit.QuantumCircuitobjects are now validated following the same rules used throughout theQuantumCircuitAPI. This was done to improve the performance of the circuits API since we can now assume thedataattribute is in a known format. If you were manually modifying thedataattribute of a circuit object before this may no longer work if your modifications resulted in a data structure other than the list of instructions with context in the format[(instruction, qargs, cargs)]The transpiler default passmanager for optimization level 2 now uses the
DenseLayoutlayout selection mechanism by default instead ofNoiseAdaptiveLayout. TheDenselayoutpass has also been modified to be made noise-aware.The deprecated
DeviceSpecificationclass has been removed. Instead you should use thePulseChannelSpec. For example, you can run something like:device = pulse.PulseChannelSpec.from_backend(backend) device.drives[0] # for DeviceSpecification, this was device.q[0].drive device.memoryslots # this was device.mem
The deprecated module
qiskit.pulse.opshas been removed. UseScheduleandInstructionmethods directly. For example, rather than:ops.union(schedule_0, schedule_1) ops.union(instruction, schedule) # etc
Instead please use:
schedule_0.union(schedule_1) instruction.union(schedule)
This same pattern applies to other
opsfunctions:insert,shift,append, andflatten.
Deprecation Notes¶
Using the
controlproperty ofqiskit.circuit.Instructionfor classical control is now deprecated. In the future this property will be used for quantum control. Classically conditioned operations will instead be handled by theconditionproperty ofqiskit.circuit.Instruction.Support for setting
qiskit.circuit.Instructionparameters with an object of typeqiskit.qasm.node.Nodehas been deprecated.Nodeobjects that were previously used as parameters should be converted to a supported type prior to initializing a newInstructionobject or calling theInstruction.paramssetter. Supported types areint,float,complex,str,qiskit.circuit.ParameterExpression, ornumpy.ndarray.In the qiskit 0.9.0 release the representation of bits (both qubits and classical bits) changed from tuples of the form
(register, index)to be instances of the classesqiskit.circuit.Qubitandqiskit.circuit.Clbit. For backwards compatibility comparing the equality between a legacy tuple and the bit classes was supported as everything transitioned from tuples to being objects. This support is now deprecated and will be removed in the future. Everything should use the bit classes instead of tuples moving forward.When the
mploutput is used for eitherqiskit.QuantumCircuit.draw()orqiskit.visualization.circuit_drawer()and thestylekwarg is used, passing in unsupported dictionary keys as part of thestyle`dictionary is now deprecated. Where these unknown arguments were previously silently ignored, in the future, unsupported keys will raise an exception.The
line lengthkwarg for theqiskit.QuantumCircuit.draw()method and theqiskit.visualization.circuit_drawer()function with the text output mode is deprecated. It has been replaced by thefoldkwarg which will behave identically for the text output mode (but also now supports the mpl output mode too).line_lengthwill be removed in a future release so calls should be updated to usefoldinstead.The
foldfield in thestyledict kwarg for theqiskit.QuantumCircuit.draw()method and theqiskit.visualization.circuit_drawer()function has been deprecated. It has been replaced by thefoldkwarg on both functions. This kwarg behaves identically to the field in the style dict.
Bug Fixes¶
Instructions layering which underlies all types of circuit drawing has changed to address right/left justification. This sometimes results in output which is topologically equivalent to the rendering in prior versions but visually different than previously rendered. Fixes issue #2802
Add
memory_slotstoQobjExperimentHeaderof pulse Qobj. This fixes a bug in the data format ofmeas_level=2results of pulse experiments. Measured quantum states are returned as a bit string with zero padding based on the number set formemory_slots.Fixed the visualization of the rzz gate in the latex circuit drawer to match the cu1 gate to reflect the symmetry in the rzz gate. The fix is based on the cds command of the qcircuit latex package. Fixes issue #1957
Other Notes¶
matplotlib.figure.Figureobjects returned by visualization functions are no longer always closed by default. Instead the returned figure objects are only closed if the configured matplotlib backend is an inline jupyter backend(either set with%matplotlib inlineor%matplotlib notebook). Output figure objects are still closed with these backends to avoid duplicate outputs in jupyter notebooks (which is why theFigure.close()were originally added).
Aer 0.3¶
No Change
Ignis 0.2¶
No Change
Aqua 0.6¶
No Change
IBM Q Provider 0.3¶
No Change
Qiskit 0.12.0¶
Terra 0.9¶
Prelude¶
The 0.9 release includes many new features and many bug fixes. The biggest
changes for this release are new debugging capabilities for PassManagers. This
includes a function to visualize a PassManager, the ability to add a callback
function to a PassManager, and logging of passes run in the PassManager.
Additionally, this release standardizes the way that you can set an initial
layout for your circuit. So now you can leverage initial_layout the kwarg
parameter on qiskit.compiler.transpile() and qiskit.execute() and the
qubits in the circuit will get laid out on the desire qubits on the device.
Visualization of circuits will now also show this clearly when visualizing a
circuit that has been transpiled with a layout.
New Features¶
A
DAGCircuitobject (i.e. the graph representation of a QuantumCircuit where operation dependencies are explicit) can now be visualized with the.draw()method. This is in line with Qiskit's philosophy of easy visualization. Other objects which support a.draw()method areQuantumCircuit,PassManager, andSchedule.Added a new visualization function
qiskit.visualization.plot_error_map()to plot the error map for a given backend. It takes in a backend object from the qiskit-ibmq-provider and will plot the current error map for that device.Both
qiskit.QuantumCircuit.draw()andqiskit.visualization.circuit_drawer()now support annotating the qubits in the visualization with layout information. If theQuantumCircuitobject being drawn includes layout metadata (which is normally only set on the circuit output fromtranspile()calls) then by default that layout will be shown on the diagram. This is done for all circuit drawer backends. For example:from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister from qiskit.compiler import transpile qr = QuantumRegister(2, 'userqr') cr = ClassicalRegister(2, 'c0') qc = QuantumCircuit(qr, cr) qc.h(qr[0]) qc.cx(qr[0], qr[1]) qc.y(qr[0]) qc.x(qr[1]) qc.measure(qr, cr) # Melbourne coupling map coupling_map = [[1, 0], [1, 2], [2, 3], [4, 3], [4, 10], [5, 4], [5, 6], [5, 9], [6, 8], [7, 8], [9, 8], [9, 10], [11, 3], [11, 10], [11, 12], [12, 2], [13, 1], [13, 12]] qc_result = transpile(qc, basis_gates=['u1', 'u2', 'u3', 'cx', 'id'], coupling_map=coupling_map, optimization_level=0) qc.draw(output='text')
will yield a diagram like:
┌──────────┐┌──────────┐┌───┐┌──────────┐┌──────────────────┐┌─┐ (userqr0) q0|0>┤ U2(0,pi) ├┤ U2(0,pi) ├┤ X ├┤ U2(0,pi) ├┤ U3(pi,pi/2,pi/2) ├┤M├─── ├──────────┤└──────────┘└─┬─┘├──────────┤└─┬─────────────┬──┘└╥┘┌─┐ (userqr1) q1|0>┤ U2(0,pi) ├──────────────■──┤ U2(0,pi) ├──┤ U3(pi,0,pi) ├────╫─┤M├ └──────────┘ └──────────┘ └─────────────┘ ║ └╥┘ (ancilla0) q2|0>──────────────────────────────────────────────────────────────╫──╫─ ║ ║ (ancilla1) q3|0>──────────────────────────────────────────────────────────────╫──╫─ ║ ║ (ancilla2) q4|0>──────────────────────────────────────────────────────────────╫──╫─ ║ ║ (ancilla3) q5|0>──────────────────────────────────────────────────────────────╫──╫─ ║ ║ (ancilla4) q6|0>──────────────────────────────────────────────────────────────╫──╫─ ║ ║ (ancilla5) q7|0>──────────────────────────────────────────────────────────────╫──╫─ ║ ║ (ancilla6) q8|0>──────────────────────────────────────────────────────────────╫──╫─ ║ ║ (ancilla7) q9|0>──────────────────────────────────────────────────────────────╫──╫─ ║ ║ (ancilla8) q10|0>──────────────────────────────────────────────────────────────╫──╫─ ║ ║ (ancilla9) q11|0>──────────────────────────────────────────────────────────────╫──╫─ ║ ║ (ancilla10) q12|0>──────────────────────────────────────────────────────────────╫──╫─ ║ ║ (ancilla11) q13|0>──────────────────────────────────────────────────────────────╫──╫─ ║ ║ c0_0: 0 ══════════════════════════════════════════════════════════════╩══╬═ ║ c0_1: 0 ═════════════════════════════════════════════════════════════════╩═If you do not want the layout to be shown on transpiled circuits (or any other circuits with a layout set) there is a new boolean kwarg for both functions,
with_layout(which defaultsTrue), which when setFalsewill disable the layout annotation in the output circuits.A new analysis pass
CountOpsLongestwas added to retrieve the number of operations on the longest path of the DAGCircuit. When used it will add acount_ops_longest_pathkey to the property set dictionary. You can add it to your a passmanager with something like:from qiskit.transpiler.passes import CountOpsLongestPath from qiskit.transpiler.passes import CxCancellation from qiskit.transpiler import PassManager pm = PassManager() pm.append(CountOpsLongestPath())
and then access the longest path via the property set value with something like:
pm.append( CxCancellation(), condition=lambda property_set: property_set[ 'count_ops_longest_path'] < 5)
which will set a condition on that pass based on the longest path.
Two new functions,
sech()andsech_deriv()were added to the pulse library moduleqiskit.pulse.pulse_libfor creating an unnormalized hyperbolic secantSamplePulseobject and an unnormalized hyperbolic secant derviativeSamplePulseobject respectively.A new kwarg option
vertical_compressionwas added to theQuantumCircuit.draw()method and theqiskit.visualization.circuit_drawer()function. This option only works with thetextbackend. This option can be set to eitherhigh,medium(the default), orlowto adjust how much vertical space is used by the output visualization.A new kwarg boolean option
idle_wireswas added to theQuantumCircuit.draw()method and theqiskit.visualization.circuit_drawer()function. It works for all drawer backends. Whenidle_wiresis set False in a drawer call the drawer will not draw any bits that do not have any circuit elements in the output quantum circuit visualization.A new PassManager visualizer function
qiskit.visualization.pass_mamanger_drawer()was added. This function takes in a PassManager object and will generate a flow control diagram of all the passes run in the PassManager.When creating a PassManager you can now specify a callback function that if specified will be run after each pass is executed. This function gets passed a set of kwargs on each call with the state of the pass manager after each pass execution. Currently these kwargs are:
pass_(Pass): the pass being rundag(DAGCircuit): the dag output of the passtime(float): the time to execute the passproperty_set(PropertySet): the property setcount(int): the index for the pass execution
However, it's worth noting that while these arguments are set for the 0.9 release they expose the internals of the pass manager and are subject to change in future release.
For example you can use this to create a callback function that will visualize the circuit output after each pass is executed:
from qiskit.transpiler import PassManager def my_callback(**kwargs): print(kwargs['dag']) pm = PassManager(callback=my_callback)
Additionally you can specify the callback function when using
qiskit.compiler.transpile():from qiskit.compiler import transpile def my_callback(**kwargs): print(kwargs['pass']) transpile(circ, callback=my_callback)
A new method
filter()was added to theqiskit.pulse.Scheduleclass. This enables filtering the instructions in a schedule. For example, filtering by instruction type:from qiskit.pulse import Schedule from qiskit.pulse.commands import Acquire from qiskit.pulse.commands import AcquireInstruction from qiskit.pulse.commands import FrameChange sched = Schedule(name='MyExperiment') sched.insert(0, FrameChange(phase=-1.57)(device)) sched.insert(60, Acquire(5)) acquire_sched = sched.filter(instruction_types=[AcquireInstruction])
Additional decomposition methods for several types of gates. These methods will use different decomposition techniques to break down a gate into a sequence of CNOTs and single qubit gates. The following methods are added:
Method
Description
QuantumCircuit.iso()Add an arbitrary isometry from m to n qubits to a circuit. This allows for attaching arbitrary unitaries on n qubits (m=n) or to prepare any state of n qubits (m=0)
QuantumCircuit.diag_gate()Add a diagonal gate to the circuit
QuantumCircuit.squ()Decompose an arbitrary 2x2 unitary into three rotation gates and add to a circuit
QuantumCircuit.ucg()Attach an uniformly controlled gate (also called a multiplexed gate) to a circuit
QuantumCircuit.ucx()Attach a uniformly controlled (also called multiplexed) Rx rotation gate to a circuit
QuantumCircuit.ucy()Attach a uniformly controlled (also called multiplexed) Ry rotation gate to a circuit
QuantumCircuit.ucz()Attach a uniformly controlled (also called multiplexed) Rz rotation gate to a circuit
Addition of Gray-Synth and Patel–Markov–Hayes algorithms for synthesis of CNOT-Phase and CNOT-only linear circuits. These functions allow the synthesis of circuits that consist of only CNOT gates given a linear function or a circuit that consists of only CNOT and phase gates given a matrix description.
A new function
random_circuitwas added to theqiskit.circuit.randommodule. This function will generate a random circuit of a specified size by randomly selecting different gates and adding them to the circuit. For example, you can use this to generate a 5-qubit circuit with a depth of 10 using:from qiskit.circuit.random import random_circuit circ = random_circuit(5, 10)
A new kwarg
output_nameswas added to theqiskit.compiler.transpile()function. This kwarg takes in a string or a list of strings and uses those as the value of the circuit name for the output circuits that get returned by thetranspile()call. For example:from qiskit.compiler import transpile my_circs = [circ_a, circ_b] tcirc_a, tcirc_b = transpile(my_circs, output_names=['Circuit A', 'Circuit B'])
the
nameattribute on tcirc_a and tcirc_b will be'Circuit A'and'Circuit B'respectively.A new method
equiv()was added to theqiskit.quantum_info.Operatorandqiskit.quantum_info.Statevectorclasses. These methods are used to check whether a secondOperatorobject orStatevectoris equivalent up to global phase.The user config file has several new options:
The
circuit_drawerfield now accepts an auto value. When set as the value for thecircuit_drawerfield the default drawer backend will be mpl if it is available, otherwise the text backend will be used.A new field
circuit_mpl_stylecan be used to set the default style used by the matplotlib circuit drawer. Valid values for this field arebwanddefaultto set the default to a black and white or the default color style respectively.A new field
transpile_optimization_levelcan be used to set the default transpiler optimization level to use for calls toqiskit.compiler.transpile(). The value can be set to either 0, 1, 2, or 3.
Introduced a new pulse command
Delaywhich may be inserted into a pulseSchedule. This command accepts adurationand may be added to anyChannel. Other commands may not be scheduled on a channel during a delay.The delay can be added just like any other pulse command. For example:
from qiskit import pulse drive_channel = pulse.DriveChannel(0) delay = pulse.Delay(20) sched = pulse.Schedule() sched += delay(drive_channel)
Upgrade Notes¶
The previously deprecated
qiskit._utilmodule has been removed.qiskit.utilshould be used instead.The
QuantumCircuit.count_ops()method now returns anOrderedDictobject instead of adict. This should be compatible for most use cases sinceOrderedDictis adictsubclass. However type checks and other class checks might need to be updated.The
DAGCircuit.width()method now returns the total number quantum bits and classical bits. Before it would only return the number of quantum bits. If you require just the number of quantum bits you can useDAGCircuit.num_qubits()instead.The function
DAGCircuit.num_cbits()has been removed. Instead you can useDAGCircuit.num_clbits().Individual quantum bits and classical bits are no longer represented as
(register, index)tuples. They are now instances of Qubit and Clbit classes. If you're dealing with individual bits make sure that you update any usage or type checks to look for these new classes instead of tuples.The preset passmanager classes
qiskit.transpiler.preset_passmanagers.default_pass_managerandqiskit.transpiler.preset_passmanagers.default_pass_manager_simulator(which were the previous default pass managers forqiskit.compiler.transpile()calls) have been removed. If you were manually using this pass managers switch to the new default,qiskit.transpile.preset_passmanagers.level1_pass_manager.The
LegacySwappass has been removed. If you were using it in a custom pass manager, it's usage can be replaced by theStochasticSwappass, which is a faster more stable version. All the preset passmanagers have been updated to useStochasticSwappass instead of theLegacySwap.The following deprecated
qiskit.dagcircuit.DAGCircuitmethods have been removed:DAGCircuit.get_qubits()- UseDAGCircuit.qubits()insteadDAGCircuit.get_bits()- UseDAGCircuit.clbits()insteadDAGCircuit.qasm()- Use a combination ofqiskit.converters.dag_to_circuit()andQuantumCircuit.qasm(). For example:from qiskit.dagcircuit import DAGCircuit from qiskit.converters import dag_to_circuit my_dag = DAGCircuit() qasm = dag_to_circuit(my_dag).qasm()
DAGCircuit.get_op_nodes()- UseDAGCircuit.op_nodes()instead. Note that the return type is a list ofDAGNodeobjects forop_nodes()instead of the list of tuples previously returned byget_op_nodes().DAGCircuit.get_gate_nodes()- UseDAGCircuit.gate_nodes()instead. Note that the return type is a list ofDAGNodeobjects forgate_nodes()instead of the list of tuples previously returned byget_gate_nodes().DAGCircuit.get_named_nodes()- UseDAGCircuit.named_nodes()instead. Note that the return type is a list ofDAGNodeobjects fornamed_nodes()instead of the list of node_ids previously returned byget_named_nodes().DAGCircuit.get_2q_nodes()- UseDAGCircuit.twoQ_gates()instead. Note that the return type is a list ofDAGNodeobjects fortwoQ_gates()instead of the list of data_dicts previously returned byget_2q_nodes().DAGCircuit.get_3q_or_more_nodes()- UseDAGCircuit.threeQ_or_more_gates()instead. Note that the return type is a list ofDAGNodeobjects forthreeQ_or_more_gates()instead of the list of tuples previously returned byget_3q_or_more_nodes().
The following
qiskit.dagcircuit.DAGCircuitmethods had deprecated support for accepting anode_idas a parameter. This has been removed and now onlyDAGNodeobjects are accepted as input:successors()predecessors()ancestors()descendants()bfs_successors()quantum_successors()remove_op_node()remove_ancestors_of()remove_descendants_of()remove_nonancestors_of()remove_nondescendants_of()substitute_node_with_dag()
The
qiskit.dagcircuit.DAGCircuitmethodrename_register()has been removed. This was unused by all the qiskit code. If you were relying on it externally you'll have to re-implement is an external function.The
qiskit.dagcircuit.DAGCircuitpropertymulti_graphhas been removed. Direct access to the underlyingnetworkxmulti_graphobject isn't supported anymore. The API provided by theDAGCircuitclass should be used instead.The deprecated exception class
qiskit.qiskiterror.QiskitErrorhas been removed. Instead you should useqiskit.exceptions.QiskitError.The boolean kwargs,
ignore_requiresandignore_preservesfrom theqiskit.transpiler.PassManagerconstructor have been removed. These are no longer valid options.The module
qiskit.tools.logginghas been removed. This module was not used by anything and added nothing over the interfaces that Python's standard libraryloggingmodule provides. If you want to set a custom formatter for logging use the standard libraryloggingmodule instead.The
CompositeGateclass has been removed. Instead you should directly create a instruction object from a circuit and append that to your circuit. For example, you can run something like:custom_gate_circ = qiskit.QuantumCircuit(2) custom_gate_circ.x(1) custom_gate_circ.h(0) custom_gate_circ.cx(0, 1) custom_gate = custom_gate_circ.to_instruction()
The previously deprecated kwargs,
seedandconfigforqiskit.compiler.assemble()have been removed useseed_simulatorandrun_configrespectively instead.The previously deprecated converters
qiskit.converters.qobj_to_circuits()andqiskit.converters.circuits_to_qobj()have been removed. Useqiskit.assembler.disassemble()andqiskit.compiler.assemble()respectively instead.The previously deprecated kwarg
seed_mapperforqiskit.compiler.transpile()has been removed. Instead you should useseed_transpilerThe previously deprecated kwargs
seed,seed_mapper,config, andcircuitsfor theqiskit.execute()function have been removed. Useseed_simulator,seed_transpiler,run_config, andexperimentsarguments respectively instead.The previously deprecated
qiskit.tools.qcvvmodule has been removed use qiskit-ignis instead.The previously deprecated functions
qiskit.transpiler.transpile()andqiskit.transpiler.transpile_dag()have been removed. Instead you should useqiskit.compiler.transpile. If you were usingtranspile_dag()this can be replaced by running:circ = qiskit.converters.dag_to_circuit(dag) out_circ = qiskit.compiler.transpile(circ) qiskit.converters.circuit_to_dag(out_circ)
The previously deprecated function
qiskit.compile()has been removed instead you should useqiskit.compiler.transpile()andqiskit.compiler.assemble().The jupyter cell magic
%%qiskit_progress_barfromqiskit.tools.jupyterhas been changed to a line magic. This was done to better reflect how the magic is used and how it works. If you were using the%%qiskit_progress_barcell magic in an existing notebook, you will have to update this to be a line magic by changing it to be%qiskit_progress_barinstead. Everything else should behave identically.The deprecated function
qiskit.tools.qi.qi.random_unitary_matrix()has been removed. You should use theqiskit.quantum_info.random.random_unitary()function instead.The deprecated function
qiskit.tools.qi.qi.random_density_matrix()has been removed. You should use theqiskit.quantum_info.random.random_density_matrix()function instead.The deprecated function
qiskit.tools.qi.qi.purity()has been removed. You should theqiskit.quantum_info.purity()function instead.The deprecated
QuantumCircuit._attach()method has been removed. You should useQuantumCircuit.append()instead.The
qiskit.qasm.Qasmmethodget_filename()has been removed. You can use thereturn_filename()method instead.The deprecated
qiskit.mappermodule has been removed. The list of functions and classes with their alternatives are:qiskit.mapper.CouplingMap:qiskit.transpiler.CouplingMapshould be used instead.qiskit.mapper.Layout:qiskit.transpiler.Layoutshould be used insteadqiskit.mapper.compiling.euler_angles_1q():qiskit.quantum_info.synthesis.euler_angles_1q()should be used insteadqiskit.mapper.compiling.two_qubit_kak():qiskit.quantum_info.synthesis.two_qubit_cnot_decompose()should be used instead.
The deprecated exception classes
qiskit.mapper.exceptions.CouplingErrorandqiskit.mapper.exceptions.LayoutErrordon't have an alternative since they serve no purpose without aqiskit.mappermodule.The
qiskit.pulse.samplersmodule has been moved toqiskit.pulse.pulse_lib.samplers. You will need to update imports ofqiskit.pulse.samplerstoqiskit.pulse.pulse_lib.samplers.seaborn is now a dependency for the function
qiskit.visualization.plot_state_qsphere(). It is needed to generate proper angular color maps for the visualization. Theqiskit-terra[visualization]extras install target has been updated to installseaborn>=0.9.0If you are using visualizations and specifically theplot_state_qsphere()function you can use that to installseabornor just manually runpip install seaborn>=0.9.0The previously deprecated functions
qiksit.visualization.plot_stateandqiskit.visualization.iplot_statehave been removed. Instead you should use the specific function for each plot type. You can refer to the following tables to map the deprecated functions to their equivalent new ones:Qiskit Terra 0.6
Qiskit Terra 0.7+
plot_state(rho)
plot_state_city(rho)
plot_state(rho, method='city')
plot_state_city(rho)
plot_state(rho, method='paulivec')
plot_state_paulivec(rho)
plot_state(rho, method='qsphere')
plot_state_qsphere(rho)
plot_state(rho, method='bloch')
plot_bloch_multivector(rho)
plot_state(rho, method='hinton')
plot_state_hinton(rho)
The
pylatexencandpillowdependencies for thelatexandlatex_sourcecircuit drawer backends are no longer listed as requirements. If you are going to use the latex circuit drawers ensure you have both packages installed or use the setuptools extras to install it along with qiskit-terra:pip install qiskit-terra[visualization]
The root of the
qiskitnamespace will now emit a warning on import if eitherqiskit.IBMQorqiskit.Aercould not be setup. This will occur whenever anything in theqiskitnamespace is imported. These warnings were added to make it clear for users up front if they're running qiskit and the qiskit-aer and qiskit-ibmq-provider packages could not be found. It's not always clear if the packages are missing or python packaging/pip installed an element incorrectly until you go to use them and get an emptyImportError. These warnings should make it clear up front if there these commonly used aliases are missing.However, for users that choose not to use either qiskit-aer or qiskit-ibmq-provider this might cause additional noise. For these users these warnings are easily suppressable using Python's standard library
warnings. Users can suppress the warnings by putting these two lines before any imports from qiskit:import warnings warnings.filterwarnings('ignore', category=RuntimeWarning, module='qiskit')
This will suppress the warnings emitted by not having qiskit-aer or qiskit-ibmq-provider installed, but still preserve any other warnings emitted by qiskit or any other package.
Deprecation Notes¶
The
UandCXgates have been deprecated. If you're using these gates in your code you should update them to useu3andcxinstead. For example, if you're using the circuit gate functionscircuit.u_base()andcircuit.cx_base()you should update these to becircuit.u3()andcircuit.cx()respectively.The
u0gate has been deprecated in favor of using multipleidengates and it will be removed in the future. If you're using theu0gate in your circuit you should update your calls to useiden. For example, f you were usingcircuit.u0(2)in your circuit before that should be updated to be:circuit.iden() circuit.iden()
instead.
The
qiskit.pulse.DeviceSpecificationclass is deprecated now. Instead you should useqiskit.pulse.PulseChannelSpec.Accessing a
qiskit.circuit.Qubit,qiskit.circuit.Clbit, orqiskit.circuit.Bitclass by index is deprecated (for compatibility with the(register, index)tuples that these classes replaced). Instead you should use theregisterandindexattributes.Passing in a bit to the
qiskit.QuantumCircuitmethodappendas a tuple(register, index)is deprecated. Instead bit objects should be used directly.Accessing the elements of a
qiskit.transpiler.Layoutobject with a tuple(register, index)is deprecated. Instead a bit object should be used directly.The
qiskit.transpiler.Layoutconstructor methodqiskit.transpiler.Layout.from_tuplelist()is deprecated. Instead the constructorqiskit.transpiler.Layout.from_qubit_list()should be used.The module
qiskit.pulse.opshas been deprecated. All the functions it provided:unionflattenshiftinsertappend
have equivalent methods available directly on the
qiskit.pulse.Scheduleandqiskit.pulse.Instructionclasses. Those methods should be used instead.The
qiskit.qasm.Qasmmethodget_tokens()is deprecated. Instead you should use thegenerate_tokens()method.The
qiskit.qasm.qasmparser.QasmParsermethodget_tokens()is deprecated. Instead you should use theread_tokens()method.The
as_dict()method for the Qobj class has been deprecated and will be removed in the future. You should replace calls to it withto_dict()instead.
Bug Fixes¶
The definition of the
CU3Gatehas been changed to be equivalent to the canonical definition of a controlledU3Gate.The handling of layout in the pass manager has been standardized. This fixes several reported issues with handling layout. The
initial_layoutkwarg parameter onqiskit.compiler.transpile()andqiskit.execute()will now lay out your qubits from the circuit onto the desired qubits on the device when transpiling circuits.Support for n-qubit unitaries was added to the BasicAer simulator and
unitary(arbitrary unitary gates) was added to the set of basis gates for the simulatorsThe
qiskit.visualization.plost_state_qsphere()has been updated to fix several issues with it. Now output Q Sphere visualization will be correctly generated and the following aspects have been updated:All complementary basis states are antipodal
Phase is indicated by color of line and marker on sphere's surface
- Probability is indicated by translucency of line and volume of marker on
sphere's surface
Other Notes¶
The default PassManager for
qiskit.compiler.transpile()andqiskit.execute()has been changed to optimization level 1 pass manager defined atqiskit.transpile.preset_passmanagers.level1_pass_manager.All the circuit drawer backends now will express gate parameters in a circuit as common fractions of pi in the output visualization. If the value of a parameter can be expressed as a fraction of pi that will be used instead of the numeric equivalent.
When using
qiskit.assembler.assemble_schedules()if you do not provide the number of memory_slots to use the number will be inferred based on the number of acquisitions in the input schedules.The deprecation warning on the
qiskit.dagcircuit.DAGCircuitpropertynode_counterhas been removed. The behavior change being warned about was put into effect when the warning was added, so warning that it had changed served no purpose.Calls to
PassManager.run()now will emit python logging messages at the INFO level for each pass execution. These messages will include the Pass name and the total execution time of the pass. Python's standard logging was used because it allows Qiskit-Terra's logging to integrate in a standard way with other applications and libraries. All logging for the transpiler occurs under theqiskit.transpilernamespace, as used bylogging.getLogger('qiskit.transpiler). For example, to turn on DEBUG level logging for the transpiler you can run:import logging logging.basicConfig() logging.getLogger('qiskit.transpiler').setLevel(logging.DEBUG)
which will set the log level for the transpiler to DEBUG and configure those messages to be printed to stderr.
Aer 0.3¶
There's a new high-performance Density Matrix Simulator that can be used in conjunction with our noise models, to better simulate real world scenarios.
We have added a Matrix Product State (MPS) simulator. MPS allows for efficient simulation of several classes of quantum circuits, even under presence of strong correlations and highly entangled states. For cases amenable to MPS, circuits with several hundred qubits and more can be exactly simulated, e.g., for the purpose of obtaining expectation values of observables.
Snapshots can be performed in all of our simulators.
Now we can measure sampling circuits with read-out errors too, not only ideal circuits.
We have increased some circuit optimizations with noise presence.
A better 2-qubit error approximations have been included.
Included some tools for making certain noisy simulations easier to craft and faster to simulate.
Increased performance with simulations that require less floating point numerical precision.
Ignis 0.2¶
New Features¶
Seed values can now be arbitrarily added to RB (not just in order)
Support for adding multiple results to measurement mitigation
RB Fitters now support providing guess values
Bug Fixes¶
Fixed a bug in RB fit error
Fixed a bug in the characterization fitter when selecting a qubit index to fit
Other Notes¶
Measurement mitigation now operates in parallel when applied to multiple results
Guess values for RB fitters are improved
Aqua 0.6¶
Added¶
Relative-Phase Toffoli gates
rccx(with 2 controls) andrcccx(with 3 controls).Variational form
RYCRXA new
'basic-no-ancilla'mode tomct.Multi-controlled rotation gates
mcrx,mcry, andmcrzas a generalu3gate is not supported by graycode implementationChemistry: ROHF open-shell support
Supported for all drivers: Gaussian16, PyQuante, PySCF and PSI4
HartreeFock initial state, UCCSD variational form and two qubit reduction for parity mapping now support different alpha and beta particle numbers for open shell support
Chemistry: UHF open-shell support
Supported for all drivers: Gaussian16, PyQuante, PySCF and PSI4
QMolecule extended to include integrals, coefficients etc for separate beta
Chemistry: QMolecule extended with integrals in atomic orbital basis to facilitate common access to these for experimentation
Supported for all drivers: Gaussian16, PyQuante, PySCF and PSI4
Chemistry: Additional PyQuante and PySCF driver configuration
Convergence tolerance and max convergence iteration controls.
For PySCF initial guess choice
Chemistry: Processing output added to debug log from PyQuante and PySCF computations (Gaussian16 and PSI4 outputs were already added to debug log)
Chemistry: Merged qiskit-chemistry into qiskit-aqua
Add
MatrixOperator,WeightedPauliOperatorandTPBGroupedPauliOperatorclass.Add
evolution_instructionfunction to get registerless instruction of time evolution.Add
op_convertermodule to unify the place in charge of converting different types of operators.Add
Z2Symmetriesclass to encapsulate the Z2 symmetries info and has helper methods for tapering an Operator.Amplitude Estimation: added maximum likelihood postprocessing and confidence interval computation.
Maximum Likelihood Amplitude Estimation (MLAE): Implemented new algorithm for amplitude estimation based on maximum likelihood estimation, which reduces number of required qubits and circuit depth.
Added (piecewise) linearly and polynomially controlled Pauli-rotation circuits.
Add
q_equation_of_motionto study excited state of a molecule, and add two algorithms to prepare the reference state.
Changed¶
Improve
mct's'basic'mode by using relative-phase Toffoli gates to build intermediate results.Adapt to Qiskit Terra's newly introduced
Qubitclass.Prevent
QPE/IQPEfrom modifying inputOperatorobjects.The PyEDA dependency was removed; corresponding oracles' underlying logic operations are now handled by SymPy.
Refactor the
Operatorclass, each representation has its own classMatrixOperator,WeightedPauliOperatorandTPBGroupedPauliOperator.The
powerinevolution_instructionwas applied on the theta on the CRZ gate directly, the new version repeats the circuits to implement power.CircuitCache is OFF by default, and it can be set via environment variable now
QISKIT_AQUA_CIRCUIT_CACHE.
Bug Fixes¶
A bug where
TruthTableOraclewould build incorrect circuits for truth tables with only a single1value.A bug caused by
PyEDA's indeterminism.A bug with
QPE/IQPE's translation and stretch computation.Chemistry: Bravyi-Kitaev mapping fixed when num qubits was not a power of 2
Setup
initial_layoutinQuantumInstancevia a list.
Removed¶
General multi-controlled rotation gate
mcu3is removed and replaced by multi-controlled rotation gatesmcrx,mcry, andmcrz
Deprecated¶
The
Operatorclass is deprecated, in favor of usingMatrixOperator,WeightedPauliOperatorandTPBGroupedPauliOperator.
IBM Q Provider 0.3¶
No change
Qiskit 0.11.1¶
We have bumped up Qiskit micro version to 0.11.1 because IBM Q Provider has bumped its micro version as well.
Terra 0.8¶
No Change
Aer 0.2¶
No change
Ignis 0.1¶
No Change
Aqua 0.5¶
qiskit-aqua has been updated to 0.5.3 to fix code related to
changes in how gates inverses are done.
IBM Q Provider 0.3¶
The IBMQProvider has been updated to version 0.3.1 to fix
backward compatibility issues and work with the default 10 job
limit in single calls to the IBM Q API v2.
Qiskit 0.11¶
We have bumped up Qiskit minor version to 0.11 because IBM Q Provider has bumped up its minor version too. On Aer, we have jumped from 0.2.1 to 0.2.3 because there was an issue detected right after releasing 0.2.2 and before Qiskit 0.11 went online.
Terra 0.8¶
No Change
Aer 0.2¶
New features¶
Added support for multi-controlled phase gates
Added optimized anti-diagonal single-qubit gates
Improvements¶
Introduced a technique called Fusion that increments performance of circuit execution Tuned threading strategy to gain performance in most common scenarios.
Some of the already implemented error models have been polished.
Ignis 0.1¶
No Change
Aqua 0.5¶
No Change
IBM Q Provider 0.3¶
The IBMQProvider has been updated in order to default to use the new
IBM Q Experience v2. Accessing the legacy IBM Q Experience v1 and QConsole
will still be supported during the 0.3.x line until its final deprecation one
month from the release. It is encouraged to update to the new IBM Q
Experience to take advantage of the new functionality and features.
Updating to the new IBM Q Experience v2¶
If you have credentials for the legacy IBM Q Experience stored on disk, you can make use of the interactive helper:
from qiskit import IBMQ
IBMQ.update_account()
For more complex cases or fine tuning your configuration, the following methods are available:
the
IBMQ.delete_accounts()can be used for resetting your configuration file.the
IBMQ.save_account('MY_TOKEN')method can be used for saving your credentials, following the instructions in the IBM Q Experience v2 account page.
Updating your programs¶
When using the new IBM Q Experience v2 through the provider, access to backends
is done via individual provider instances (as opposed to accessing them
directly through the qiskit.IBMQ object as in previous versions), which
allows for more granular control over the project you are using.
You can get a reference to the providers that you have access to using the
IBMQ.providers() and IBMQ.get_provider() methods:
from qiskit import IBMQ
provider = IBMQ.load_account()
my_providers = IBMQ.providers()
provider_2 = IBMQ.get_provider(hub='A', group='B', project='C')
For convenience, IBMQ.load_account() and IBMQ.enable_account() will
return a provider for the open access project, which is the default in the new
IBM Q Experience v2.
For example, the following program in previous versions:
from qiskit import IBMQ
IBMQ.load_accounts()
backend = IBMQ.get_backend('ibmqx4')
backend_2 = IBMQ.get_backend('ibmq_qasm_simulator', hub='HUB2')
Would be equivalent to the following program in the current version:
from qiskit import IBMQ
provider = IBMQ.load_account()
backend = provider.get_backend('ibmqx4')
provider_2 = IBMQ.get_provider(hub='HUB2')
backend_2 = provider_2.get_backend('ibmq_qasm_simulator')
You can find more information and details in the IBM Q Provider documentation.
Qiskit 0.10¶
Terra 0.8¶
No Change
Aer 0.2¶
No Change
Ignis 0.1¶
No Change
Aqua 0.5¶
No Change
Qiskit 0.9¶
Terra 0.8¶
Highlights¶
Introduction of the Pulse module under
qiskit.pulse, which includes tools for building pulse commands, scheduling them on pulse channels, visualization, and running them on IBM Q devices.Improved QuantumCircuit and Instruction classes, allowing for the composition of arbitrary sub-circuits into larger circuits, and also for creating parameterized circuits.
A powerful Quantum Info module under
qiskit.quantum_info, providing tools to work with operators and channels and to use them inside circuits.New transpiler optimization passes and access to predefined transpiling routines.
New Features¶
The core
StochasticSwaproutine is implemented in Cython.Added
QuantumChannelclasses for manipulating quantum channels and CPTP maps.Support for parameterized circuits.
The
PassManagerinterface has been improved and new functions added for easier interaction and usage with custom pass managers.Preset
PassManagers are now included which offer a predetermined pipeline of transpiler passes.User configuration files to let local environments override default values for some functions.
New transpiler passes:
EnlargeWithAncilla,Unroll2Q,NoiseAdaptiveLayout,OptimizeSwapBeforeMeasure,RemoveDiagonalGatesBeforeMeasure,CommutativeCancellation,Collect2qBlocks, andConsolidateBlocks.
Compatibility Considerations¶
As part of the 0.8 release the following things have been deprecated and will either be removed or changed in a backwards incompatible manner in a future release. While not strictly necessary these are things to adjust for before the 0.9 (unless otherwise noted) release to avoid a breaking change in the future.
The methods prefixed by
_getin theDAGCircuitobject are being renamed without that prefix.Changed elements in
couplinglistofCouplingMapfrom tuples to lists.Unroller bases must now be explicit, and violation raises an informative
QiskitError.The
qiskit.tools.qcvvpackage is deprecated and will be removed in the in the future. You should migrate to using the Qiskit Ignis which replaces this module.The
qiskit.compile()function is now deprecated in favor of explicitly using theqiskit.compiler.transpile()function to transform a circuit, followed byqiskit.compiler.assemble()to make a Qobj out of it. Instead ofcompile(...), useassemble(transpile(...), ...).qiskit.converters.qobj_to_circuits()has been deprecated and will be removed in a future release. Insteadqiskit.assembler.disassemble()should be used to extractQuantumCircuitobjects from a compiled Qobj.The
qiskit.mappernamespace has been deprecated. TheLayoutandCouplingMapclasses can be accessed viaqiskit.transpiler.A few functions in
qiskit.tools.qi.qihave been deprecated and moved toqiskit.quantum_info.
Please note that some backwards incompatible changes have been made during this release. The following notes contain information on how to adapt to these changes.
IBM Q Provider¶
The IBM Q provider was previously included in Terra, but it has been split out
into a separate package qiskit-ibmq-provider. This will need to be
installed, either via pypi with pip install qiskit-ibmq-provider or from
source in order to access qiskit.IBMQ or qiskit.providers.ibmq. If you
install qiskit with pip install qiskit, that will automatically install
all subpackages of the Qiskit project.
Cython Components¶
Starting in the 0.8 release the core stochastic swap routine is now implemented
in Cython. This was done to significantly improve the performance of the
swapper, however if you build Terra from source or run on a non-x86 or other
platform without prebuilt wheels and install from source distribution you'll
need to make sure that you have Cython installed prior to installing/building
Qiskit Terra. This can easily be done with pip/pypi: pip install Cython.
Compiler Workflow¶
The qiskit.compile() function has been deprecated and replaced by first
calling qiskit.compiler.transpile() to run optimization and mapping on a
circuit, and then qiskit.compiler.assemble() to build a Qobj from that
optimized circuit to send to a backend. While this is only a deprecation it
will emit a warning if you use the old qiskit.compile() call.
transpile(), assemble(), execute() parameters
These functions are heavily overloaded and accept a wide range of inputs.
They can handle circuit and pulse inputs. All kwargs except for backend
for these functions now also accept lists of the previously accepted types.
The initial_layout kwarg can now be supplied as a both a list and dictionary,
e.g. to map a Bell experiment on qubits 13 and 14, you can supply:
initial_layout=[13, 14] or initial_layout={qr[0]: 13, qr[1]: 14}
Qobj¶
The Qobj class has been split into two separate subclasses depending on the
use case, either PulseQobj or QasmQobj for pulse and circuit jobs
respectively. If you're interacting with Qobj directly you may need to
adjust your usage accordingly.
The qiskit.qobj.qobj_to_dict() is removed. Instead use the to_dict()
method of a Qobj object.
Visualization¶
The largest change to the visualization module is it has moved from
qiskit.tools.visualization to qiskit.visualization. This was done to
indicate that the visualization module is more than just a tool. However, since
this interface was declared stable in the 0.7 release the public interface off
of qiskit.tools.visualization will continue to work. That may change in a
future release, but it will be deprecated prior to removal if that happens.
The previously deprecated functions, plot_circuit(),
latex_circuit_drawer(), generate_latex_source(), and
matplotlib_circuit_drawer() from qiskit.tools.visualization have been
removed. Instead of these functions, calling
qiskit.visualization.circuit_drawer() with the appropriate arguments should
be used.
The previously deprecated plot_barriers and reverse_bits keys in
the style kwarg dictionary are deprecated, instead the
qiskit.visualization.circuit_drawer() kwargs plot_barriers and
reverse_bits should be used.
The Wigner plotting functions plot_wigner_function, plot_wigner_curve,
plot_wigner_plaquette, and plot_wigner_data previously in the
qiskit.tools.visualization._state_visualization module have been removed.
They were never exposed through the public stable interface and were not well
documented. The code to use this feature can still be accessed through the
qiskit-tutorials repository.
Mapper¶
The public api from qiskit.mapper has been moved into qiskit.transpiler.
While it has only been deprecated in this release, it will be removed in the
0.9 release so updating your usage of Layout and CouplingMap to import
from qiskit.transpiler instead of qiskit.mapper before that takes place
will avoid any surprises in the future.
Aer 0.2¶
New Features¶
Added multiplexer gate #192
Added
remap_noise_modelfunction tonoise.utils#181Added
__eq__method toNoiseModel,QuantumError,ReadoutError#181Added support for labelled gates in noise models #175
Added optimized
mcx,mcy,mcz,mcu1,mcu2,mcu3, gates toQubitVector#124Added optimized controlled-swap gate to
QubitVector#142Added gate-fusion optimization for
QasmController, which is enabled by settingfusion_enable=true#136Added better management of failed simulations #167
Added qubits truncate optimization for unused qubits #164
Added ability to disable depolarizing error on device noise model #131
Added initialize simulator instruction to
statevector_state#117, #137Added coupling maps to simulators #93
Added circuit optimization framework #83
Added wheels support for Debian-like distributions #69
Added autoconfiguration of threads for qasm simulator #61
Added Simulation method based on Stabilizer Rank Decompositions #51
Added
basis_gateskwarg toNoiseModelinit #175.Added an optional parameter to
NoiseModel.as_dict()for returning dictionaries that can be serialized using the standard json library directly #165Refactor thread management #50
Improve noise transformations #162
Improve error reporting #160
Improve efficiency of parallelization with
max_memory_mba new parameter ofbackend_opts#61Improve u1 performance in
statevector#123
Ignis 0.1¶
New Features¶
Quantum volume
Measurement mitigation using tensored calibrations
Simultaneous RB has the option to align Clifford gates across subsets
Measurement correction can produce a new calibration for a subset of qubits
Compatibility Considerations¶
RB writes to the minimal set of classical registers (it used to be Q[i]->C[i]). This change enables measurement correction with RB. Unless users had external analysis code, this will not change outcomes. RB circuits from 0.1 are not compatible with 0.1.1 fitters.
Aqua 0.5¶
New Features¶
Implementation of the HHL algorithm supporting
LinearSystemInputPluggable component
Eigenvalueswith variantEigQPEPluggable component
Reciprocalwith variantsLookupRotationandLongDivisionMultiple-Controlled U1 and U3 operations
mcu1andmcu3Pluggable component
QFTderived from componentIQFTSummarized the transpiled circuits at the DEBUG logging level
QuantumInstanceacceptsbasis_gatesandcoupling_mapagain.Support to use
cxgate for the entanglement inRYandRYRZvariational form (czis the default choice)Support to use arbitrary mixer Hamiltonian in QAOA, allowing use of QAOA in constrained optimization problems [arXiv:1709.03489]
Added variational algorithm base class
VQAlgorithm, implemented byVQEandQSVMVariationalAdded
ising/docplex.pyfor automatically generating Ising Hamiltonian from optimization models of DOcplexAdded
'basic-dirty-ancilla' mode formctAdded
mcmtfor Multi-Controlled, Multi-Target gateExposed capabilities to generate circuits from logical AND, OR, DNF (disjunctive normal forms), and CNF (conjunctive normal forms) formulae
Added the capability to generate circuits from ESOP (exclusive sum of products) formulae with optional optimization based on Quine-McCluskey and ExactCover
Added
LogicalExpressionOraclefor generating oracle circuits from arbitrary Boolean logic expressions (including DIMACS support) with optional optimization capabilityAdded
TruthTableOraclefor generating oracle circuits from truth-tables with optional optimization capabilityAdded
CustomCircuitOraclefor generating oracle from user specified circuitsAdded implementation of the Deutsch-Jozsa algorithm
Added implementation of the Bernstein-Vazirani algorithm
Added implementation of the Simon's algorithm
Added implementation of the Shor's algorithm
Added optional capability for Grover's algorithm to take a custom initial state (as opposed to the default uniform superposition)
Added capability to create a
Custominitial state using existing circuitAdded the ADAM (and AMSGRAD) optimization algorithm
Multivariate distributions added, so uncertainty models now have univariate and multivariate distribution components
Added option to include or skip the swaps operations for qft and iqft circuit constructions
Added classical linear system solver
ExactLSsolverAdded parameters
auto_hermitianandauto_resizetoHHLalgorithm to support non-Hermitian and non \(2^n\) sized matrices by defaultAdded another feature map,
RawFeatureVector, that directly maps feature vectors to qubits' states for classificationSVM_Classicalcan now load models trained byQSVM
Bug Fixes¶
Fixed
ising/docplex.pyto correctly multiply constant values in constraintsFixed package setup to correctly identify namespace packages using
setuptools.find_namespace_packages
Compatibility Considerations¶
QuantumInstancedoes not takememoryanymore.Moved command line and GUI to separate repo (
qiskit_aqua_uis)Removed the
SAT-specific oracle (now supported byLogicalExpressionOracle)Changed
advancedmode implementation ofmct: using simplehgates instead ofch, and fixing the old recursion step in_multicxComponents
random_distributionsrenamed touncertainty_modelsReorganized the constructions of various common gates (
ch,cry,mcry,mct,mcu1,mcu3,mcmt,logic_and, andlogic_or) and circuits (PhaseEstimationCircuit,BooleanLogicCircuits,FourierTransformCircuits, andStateVectorCircuits) under thecircuitsdirectoryRenamed the algorithm
QSVMVariationaltoVQC, which stands for Variational Quantum ClassifierRenamed the algorithm
QSVMKerneltoQSVMRenamed the class
SVMInputtoClassificationInputRenamed problem type
'svm_classification'to'classification'Changed the type of
entangler_mapused inFeatureMapandVariationalFormto list of lists
IBM Q Provider 0.1¶
New Features¶
This is the first release as a standalone package. If you are installing Terra standalone you'll also need to install the
qiskit-ibmq-providerpackage withpip install qiskit-ibmq-providerif you want to use the IBM Q backends.Support for non-Qobj format jobs has been removed from the provider. You'll have to convert submissions in an older format to Qobj before you can submit.
Qiskit 0.8¶
In Qiskit 0.8 we introduced the Qiskit Ignis element. It also includes the Qiskit Terra element 0.7.1 release which contains a bug fix for the BasicAer Python simulator.
Terra 0.7¶
No Change
Aer 0.1¶
No Change
Ignis 0.1¶
This is the first release of Qiskit Ignis.
Qiskit 0.7¶
In Qiskit 0.7 we introduced Qiskit Aer and combined it with Qiskit Terra.
Terra 0.7¶
New Features¶
This release includes several new features and many bug fixes. With this release the interfaces for circuit diagram, histogram, bloch vectors, and state visualizations are declared stable. Additionally, this release includes a defined and standardized bit order/endianness throughout all aspects of Qiskit. These are all declared as stable interfaces in this release which won't have breaking changes made moving forward, unless there is appropriate and lengthy deprecation periods warning of any coming changes.
There is also the introduction of the following new features:
A new ASCII art circuit drawing output mode
A new circuit drawing interface off of
QuantumCircuitobjects that enables calls ofcircuit.draw()orprint(circuit)to render a drawing of circuitsA visualizer for drawing the DAG representation of a circuit
A new quantum state plot type for hinton diagrams in the local matplotlib based state plots
2 new constructor methods off the
QuantumCircuitclassfrom_qasm_str()andfrom_qasm_file()which let you easily create a circuit object from OpenQASMA new function
plot_bloch_multivector()to plot Bloch vectors from a tensored state vector or density matrixPer-shot measurement results are available in simulators and select devices. These can be accessed by setting the
memorykwarg toTruewhen callingcompile()orexecute()and then accessed using theget_memory()method on theResultobject.A
qiskit.quantum_infomodule with revamped Pauli objects and methods for working with quantum statesNew transpile passes for circuit analysis and transformation:
CommutationAnalysis,CommutationTransformation,CXCancellation,Decompose,Unroll,Optimize1QGates,CheckMap,CXDirection,BarrierBeforeFinalMeasurementsNew alternative swap mapper passes in the transpiler:
BasicSwap,LookaheadSwap,StochasticSwapMore advanced transpiler infrastructure with support for analysis passes, transformation passes, a global
property_setfor the pass manager, and repeat-until control of passes
Compatibility Considerations¶
As part of the 0.7 release the following things have been deprecated and will either be removed or changed in a backwards incompatible manner in a future release. While not strictly necessary these are things to adjust for before the next release to avoid a breaking change.
plot_circuit(),latex_circuit_drawer(),generate_latex_source(), andmatplotlib_circuit_drawer()from qiskit.tools.visualization are deprecated. Instead thecircuit_drawer()function from the same module should be used, there are kwarg options to mirror the functionality of all the deprecated functions.The current default output of
circuit_drawer()(using latex and falling back on python) is deprecated and will be changed to just use thetextoutput by default in future releases.The
qiskit.wrapper.load_qasm_string()andqiskit.wrapper.load_qasm_file()functions are deprecated and theQuantumCircuit.from_qasm_str()andQuantumCircuit.from_qasm_file()constructor methods should be used instead.The
plot_barriersandreverse_bitskeys in thestylekwarg dictionary are deprecated, instead theqiskit.tools.visualization.circuit_drawer()kwargsplot_barriersandreverse_bitsshould be used instead.The functions
plot_state()andiplot_state()have been depreciated. Instead the functionsplot_state_*()andiplot_state_*()should be called for the visualization method required.The
skip_transpilerargument has been deprecated fromcompile()andexecute(). Instead you can use thePassManagerdirectly, just set thepass_managerto a blankPassManagerobject withPassManager()The
transpile_dag()functionformatkwarg for emitting different output formats is deprecated, instead you should convert the default outputDAGCircuitobject to the desired format.The unrollers have been deprecated, moving forward only DAG to DAG unrolling will be supported.
Please note that some backwards-incompatible changes have been made during this release. The following notes contain information on how to adapt to these changes.
Changes to Result objects¶
As part of the rewrite of the Results object to be more consistent and a
stable interface moving forward a few changes have been made to how you access
the data stored in the result object. First the get_data() method has been
renamed to just data(). Accompanying that change is a change in the data
format returned by the function. It is now returning the raw data from the
backends instead of doing any post-processing. For example, in previous
versions you could call:
result = execute(circuit, backend).result()
unitary = result.get_data()['unitary']
print(unitary)
and that would return the unitary matrix like:
[[1+0j, 0+0.5j], [0-0.5j][-1+0j]]
But now if you call (with the renamed method):
result.data()['unitary']
it will return something like:
[[[1, 0], [0, -0.5]], [[0, -0.5], [-1, 0]]]
To get the post processed results in the same format as before the 0.7 release
you must use the get_counts(), get_statevector(), and get_unitary()
methods on the result object instead of get_data()['counts'],
get_data()['statevector'], and get_data()['unitary'] respectively.
Additionally, support for len() and indexing on a Result object has
been removed. Instead you should deal with the output from the post processed
methods on the Result objects.
Also, the get_snapshot() and get_snapshots() methods from the
Result class have been removed. Instead you can access the snapshots
using Result.data()['snapshots'].
Changes to Visualization¶
The largest change made to visualization in the 0.7 release is the removal of
Matplotlib and other visualization dependencies from the project requirements.
This was done to simplify the requirements and configuration required for
installing Qiskit. If you plan to use any visualizations (including all the
jupyter magics) except for the text, latex, and latex_source
output for the circuit drawer you'll you must manually ensure that
the visualization dependencies are installed. You can leverage the optional
requirements to the Qiskit Terra package to do this:
pip install qiskit-terra[visualization]
Aside from this there have been changes made to several of the interfaces
as part of the stabilization which may have an impact on existing code.
The first is the basis kwarg in the circuit_drawer() function
is no longer accepted. If you were relying on the circuit_drawer() to
adjust the basis gates used in drawing a circuit diagram you will have to
do this priort to calling circuit_drawer(). For example:
from qiskit.tools import visualization
visualization.circuit_drawer(circuit, basis_gates='x,U,CX')
will have to be adjusted to be:
from qiskit import BasicAer
from qiskit import transpiler
from qiskit.tools import visualization
backend = BasicAer.backend('qasm_simulator')
draw_circ = transpiler.transpile(circuit, backend, basis_gates='x,U,CX')
visualization.circuit_drawer(draw_circ)
Moving forward the circuit_drawer() function will be the sole interface
for circuit drawing in the visualization module. Prior to the 0.7 release there
were several other functions which either used different output backends or
changed the output for drawing circuits. However, all those other functions
have been deprecated and that functionality has been integrated as options
on circuit_drawer().
For the other visualization functions, plot_histogram() and
plot_state() there are also a few changes to check when upgrading. First
is the output from these functions has changed, in prior releases these would
interactively show the output visualization. However that has changed to
instead return a matplotlib.Figure object. This provides much more
flexibility and options to interact with the visualization prior to saving or
showing it. This will require adjustment to how these functions are consumed.
For example, prior to this release when calling:
plot_histogram(counts)
plot_state(rho)
would open up new windows (depending on matplotlib backend) to display the
visualization. However starting in the 0.7 you'll have to call show() on
the output to mirror this behavior. For example:
plot_histogram(counts).show()
plot_state(rho).show()
or:
hist_fig = plot_histogram(counts)
state_fig = plot_state(rho)
hist_fig.show()
state_fig.show()
Note that this is only for when running outside of Jupyter. No adjustment is
required inside a Jupyter environment because Jupyter notebooks natively
understand how to render matplotlib.Figure objects.
However, returning the Figure object provides additional flexibility for
dealing with the output. For example instead of just showing the figure you
can now directly save it to a file by leveraging the savefig() method.
For example:
hist_fig = plot_histogram(counts)
state_fig = plot_state(rho)
hist_fig.savefig('histogram.png')
state_fig.savefig('state_plot.png')
The other key aspect which has changed with these functions is when running
under jupyter. In the 0.6 release plot_state() and plot_histogram()
when running under jupyter the default behavior was to use the interactive
Javascript plots if the externally hosted Javascript library for rendering
the visualization was reachable over the network. If not it would just use
the matplotlib version. However in the 0.7 release this no longer the case,
and separate functions for the interactive plots, iplot_state() and
iplot_histogram() are to be used instead. plot_state() and
plot_histogram() always use the matplotlib versions.
Additionally, starting in this release the plot_state() function is
deprecated in favor of calling individual methods for each method of plotting
a quantum state. While the plot_state() function will continue to work
until the 0.9 release, it will emit a warning each time it is used. The
Qiskit Terra 0.6 |
Qiskit Terra 0.7+ |
|---|---|
plot_state(rho) |
plot_state_city(rho) |
plot_state(rho, method='city') |
plot_state_city(rho) |
plot_state(rho, method='paulivec') |
plot_state_paulivec(rho) |
plot_state(rho, method='qsphere') |
plot_state_qsphere(rho) |
plot_state(rho, method='bloch') |
plot_bloch_multivector(rho) |
plot_state(rho, method='hinton') |
plot_state_hinton(rho) |
The same is true for the interactive JS equivalent, iplot_state(). The
function names are all the same, just with a prepended i for each function.
For example, iplot_state(rho, method='paulivec') is
iplot_state_paulivec(rho).
Changes to Backends¶
With the improvements made in the 0.7 release there are a few things related
to backends to keep in mind when upgrading. The biggest change is the
restructuring of the provider instances in the root qiskit` namespace.
The Aer provider is not installed by default and requires the installation
of the qiskit-aer package. This package contains the new high performance
fully featured simulator. If you installed via pip install qiskit you'll
already have this installed. The python simulators are now available under
qiskit.BasicAer and the old C++ simulators are available with
qiskit.LegacySimulators. This also means that the implicit fallback to
python based simulators when the C++ simulators are not found doesn't exist
anymore. If you ask for a local C++ based simulator backend, and it can't be
found an exception will be raised instead of just using the python simulator
instead.
Additionally the previously deprecation top level functions register() and
available_backends() have been removed. Also, the deprecated
backend.parameters() and backend.calibration() methods have been
removed in favor of backend.properties(). You can refer to the 0.6 release
notes section Working with backends for more details on these changes.
The backend.jobs() and backend.retrieve_jobs() calls no longer return
results from those jobs. Instead you must call the result() method on the
returned jobs objects.
Changes to the compiler, transpiler, and unrollers¶
As part of an effort to stabilize the compiler interfaces there have been
several changes to be aware of when leveraging the compiler functions.
First it is important to note that the qiskit.transpiler.transpile()
function now takes a QuantumCircuit object (or a list of them) and returns
a QuantumCircuit object (or a list of them). The DAG processing is done
internally now.
You can also easily switch between circuits, DAGs, and Qobj now using the
functions in qiskit.converters.
Aer 0.1¶
New Features¶
Aer provides three simulator backends:
QasmSimulator: simulate experiments and return measurement outcomesStatevectorSimulator: return the final statevector for a quantum circuit acting on the all zero stateUnitarySimulator: return the unitary matrix for a quantum circuit
noise module: contains advanced noise modeling features for the
QasmSimulator
NoiseModel,QuantumError,ReadoutErrorclasses for simulating a Qiskit quantum circuit in the presence of errorserrorssubmodule including functions for generatingQuantumErrorobjects for the following types of quantum errors: Kraus, mixed unitary, coherent unitary, Pauli, depolarizing, thermal relaxation, amplitude damping, phase damping, combined phase and amplitude dampingdevicesubmodule for automatically generating a noise model based on theBackendPropertiesof a device
utils module:
qobj_utilsprovides functions for directly modifying a Qobj to insert special simulator instructions not yet supported through the Qiskit Terra API.
Aqua 0.4¶
New Features¶
Programmatic APIs for algorithms and components -- each component can now be instantiated and initialized via a single (non-empty) constructor call
QuantumInstanceAPI for algorithm/backend decoupling --QuantumInstanceencapsulates a backend and its settingsUpdated documentation and Jupyter Notebooks illustrating the new programmatic APIs
Transparent parallelization for gradient-based optimizers
Multiple-Controlled-NOT (cnx) operation
Pluggable algorithmic component
RandomDistributionConcrete implementations of
RandomDistribution:BernoulliDistribution,LogNormalDistribution,MultivariateDistribution,MultivariateNormalDistribution,MultivariateUniformDistribution,NormalDistribution,UniformDistribution, andUnivariateDistributionConcrete implementations of
UncertaintyProblem:FixedIncomeExpectedValue,EuropeanCallExpectedValue, andEuropeanCallDeltaAmplitude Estimation algorithm
Qiskit Optimization: New Ising models for optimization problems exact cover, set packing, vertex cover, clique, and graph partition
Qiskit AI:
New feature maps extending the
FeatureMappluggable interface:PauliExpansionandPauliZExpansionTraining model serialization/deserialization mechanism
Qiskit Finance:
Amplitude estimation for Bernoulli random variable: illustration of amplitude estimation on a single qubit problem
Loading of multiple univariate and multivariate random distributions
European call option: expected value and delta (using univariate distributions)
Fixed income asset pricing: expected value (using multivariate distributions)
The Pauli string in
Operatorclass is aligned with Terra 0.7. Now the order of a n-qubit pauli string isq_{n-1}...q{0}Thus, the (de)serialier (save_to_dictandload_from_dict) in theOperatorclass are also changed to adopt the changes ofPauliclass.
Compatibility Considerations¶
HartreeFockcomponent of pluggable typeInitialStatemoved to Qiskit ChemistryUCCSDcomponent of pluggable typeVariationalFormmoved to Qiskit Chemistry
Qiskit 0.6¶
Terra 0.6¶
Highlights¶
This release includes a redesign of internal components centered around a new, formal communication format (Qobj), along with long awaited features to improve the user experience as a whole. The highlights, compared to the 0.5 release, are:
Improvements for inter-operability (based on the Qobj specification) and extensibility (facilities for extending Qiskit with new backends in a seamless way)
New options for handling credentials and authentication for the IBM Q backends, aimed at simplifying the process and supporting automatic loading of user credentials
A revamp of the visualization utilities: stylish interactive visualizations are now available for Jupyter users, along with refinements for the circuit drawer (including a matplotlib-based version)
Performance improvements centered around circuit transpilation: the basis for a more flexible and modular architecture have been set, including parallelization of the circuit compilation and numerous optimizations
Compatibility Considerations¶
Please note that some backwards-incompatible changes have been introduced during this release -- the following notes contain information on how to adapt to the new changes.
Removal of QuantumProgram¶
As hinted during the 0.5 release, the deprecation of the QuantumProgram
class has now been completed and is no longer available, in favor of working
with the individual components (BaseJob,
QuantumCircuit,
ClassicalRegister,
QuantumRegister,
qiskit) directly.
Please check the 0.5 release notes and the examples for details about the transition:
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit import Aer, execute
q = QuantumRegister(2)
c = ClassicalRegister(2)
qc = QuantumCircuit(q, c)
qc.h(q[0])
qc.cx(q[0], q[1])
qc.measure(q, c)
backend = get_backend('qasm_simulator')
job_sim = execute(qc, backend)
sim_result = job_sim.result()
print("simulation: ", sim_result)
print(sim_result.get_counts(qc))
IBM Q Authentication and Qconfig.py¶
The managing of credentials for authenticating when using the IBM Q backends has been expanded, and there are new options that can be used for convenience:
save your credentials in disk once, and automatically load them in future sessions. This provides a one-off mechanism:
from qiskit import IBMQ IBMQ.save_account('MY_API_TOKEN', 'MY_API_URL')
afterwards, your credentials can be automatically loaded from disk by invoking
load_accounts():from qiskit import IBMQ IBMQ.load_accounts()
or you can load only specific accounts if you only want to use those in a session:
IBMQ.load_accounts(project='MY_PROJECT')
use environment variables. If
QE_TOKENandQE_URLis set, theIBMQ.load_accounts()call will automatically load the credentials from them.
Additionally, the previous method of having a Qconfig.py file in the
program folder and passing the credentials explicitly is still supported.
Working with backends¶
A new mechanism has been introduced in Terra 0.6 as the recommended way for
obtaining a backend, allowing for more powerful and unified filtering and
integrated with the new credentials system. The previous top-level methods
register(),
available_backends() and
get_backend() are still supported, but will
deprecated in upcoming versions in favor of using the qiskit.IBMQ and
qiskit.Aer objects directly, which allow for more complex filtering.
For example, to list and use a local backend:
from qiskit import Aer
all_local_backends = Aer.backends(local=True) # returns a list of instances
qasm_simulator = Aer.backends('qasm_simulator')
And for listing and using remote backends:
from qiskit import IBMQ
IBMQ.enable_account('MY_API_TOKEN')
5_qubit_devices = IBMQ.backends(simulator=True, n_qubits=5)
ibmqx4 = IBMQ.get_backend('ibmqx4')
Please note as well that the names of the local simulators have been simplified. The previous names can still be used, but it is encouraged to use the new, shorter names:
Qiskit Terra 0.5 |
Qiskit Terra 0.6 |
|---|---|
'local_qasm_simulator' |
'qasm_simulator' |
'local_statevector_simulator' |
'statevector_simulator' |
'local_unitary_simulator_py' |
'unitary_simulator' |
Backend and Job API changes¶
Jobs submitted to IBM Q backends have improved capabilities. It is possible to cancel them and replenish credits (
job.cancel()), and to retrieve previous jobs executed on a specific backend either by job id (backend.retrieve_job(job_id)) or in batch of latest jobs (backend.jobs(limit))Properties for checking each individual job status (
queued,running,validating,doneandcancelled) no longer exist. If you want to check the job status, use the identity comparison againstjob.status:from qiskit.backends import JobStatus job = execute(circuit, backend) if job.status() is JobStatus.RUNNING: handle_job(job)
Please consult the new documentation of the
IBMQJob class to get further insight
in how to use the simplified API.
A number of members of
BaseBackendandBaseJobare no longer properties, but methods, and as a result they need to be invoked as functions.Qiskit Terra 0.5
Qiskit Terra 0.6
backend.name
backend.name()
backend.status
backend.status()
backend.configuration
backend.configuration()
backend.calibration
backend.properties()
backend.parameters
backend.jobs() backend.retrieve_job(job_id)
job.status
job.status()
job.cancelled
job.queue_position()
job.running
job.cancel()
job.queued
job.done
Better Jupyter tools¶
The new release contains improvements to the user experience while using Jupyter notebooks.
First, new interactive visualizations of counts histograms and quantum states
are provided:
plot_histogram() and
plot_state().
These methods will default to the new interactive kind when the environment
is Jupyter and internet connection exists.
Secondly, the new release provides Jupyter cell magics for keeping track of
the progress of your code. Use %%qiskit_job_status to keep track of the
status of submitted jobs to IBM Q backends. Use %%qiskit_progress_bar to
keep track of the progress of compilation/execution.
Qiskit 0.5¶
Terra 0.5¶
Highlights¶
This release brings a number of improvements to Qiskit, both for the user experience and under the hood. Please refer to the full changelog for a detailed description of the changes - the highlights are:
new
statevectorsimulatorsand feature and performance improvements to the existing ones (in particular to the C++ simulator), along with a reorganization of how to work with backends focused on extensibility and flexibility (using aliases and backend providers)reorganization of the asynchronous features, providing a friendlier interface for running jobs asynchronously via
Jobinstancesnumerous improvements and fixes throughout the Terra as a whole, both for convenience of the users (such as allowing anonymous registers) and for enhanced functionality (such as improved plotting of circuits)
Compatibility Considerations¶
Please note that several backwards-incompatible changes have been introduced during this release as a result of the ongoing development. While some of these features will continue to be supported during a period of time before being fully deprecated, it is recommended to update your programs in order to prepare for the new versions and take advantage of the new functionality.
QuantumProgram changes¶
Several methods of the QuantumProgram class are on their way
to being deprecated:
methods for interacting with the backends and the API:
The recommended way for opening a connection to the IBM Q API and for using the backends is through the top-level functions directly instead of the
QuantumProgrammethods. In particular, theqiskit.register()method provides the equivalent of the previousqiskit.QuantumProgram.set_api()call. In a similar vein, there is a newqiskit.available_backends(),qiskit.get_backend()and related functions for querying the available backends directly. For example, the following snippet for version 0.4:from qiskit import QuantumProgram quantum_program = QuantumProgram() quantum_program.set_api(token, url) backends = quantum_program.available_backends() print(quantum_program.get_backend_status('ibmqx4')
would be equivalent to the following snippet for version 0.5:
from qiskit import register, available_backends, get_backend register(token, url) backends = available_backends() backend = get_backend('ibmqx4') print(backend.status)
methods for compiling and executing programs:
The top-level functions now also provide equivalents for the
qiskit.QuantumProgram.compile()andqiskit.QuantumProgram.execute()methods. For example, the following snippet from version 0.4:quantum_program.execute(circuit, args, ...)
would be equivalent to the following snippet for version 0.5:
from qiskit import execute execute(circuit, args, ...)
In general, from version 0.5 onwards we encourage to try to make use of the
individual objects and classes directly instead of relying on
QuantumProgram. For example, a QuantumCircuit can be
instantiated and constructed by appending QuantumRegister,
ClassicalRegister, and gates directly. Please check the
update example in the Quickstart section, or the
using_qiskit_core_level_0.py and using_qiskit_core_level_1.py
examples on the main repository.
Backend name changes¶
In order to provide a more extensible framework for backends, there have been some design changes accordingly:
local simulator names
The names of the local simulators have been homogenized in order to follow the same pattern:
PROVIDERNAME_TYPE_simulator_LANGUAGEORPROJECT- for example, the C++ simulator previously namedlocal_qiskit_simulatoris nowlocal_qasm_simulator_cpp. An overview of the current simulators:QASMsimulator is supposed to be like an experiment. You apply a circuit on some qubits, and observe measurement results - and you repeat for many shots to get a histogram of counts viaresult.get_counts().Statevectorsimulator is to get the full statevector (\(2^n\) amplitudes) after evolving the zero state through the circuit, and can be obtained viaresult.get_statevector().Unitarysimulator is to get the unitary matrix equivalent of the circuit, returned viaresult.get_unitary().In addition, you can get intermediate states from a simulator by applying a
snapshot(slot)instruction at various spots in the circuit. This will save the current state of the simulator in a given slot, which can later be retrieved viaresult.get_snapshot(slot).
backend aliases:
The SDK now provides an "alias" system that allows for automatically using the most performant simulator of a specific type, if it is available in your system. For example, with the following snippet:
from qiskit import get_backend backend = get_backend('local_statevector_simulator')
the backend will be the C++ statevector simulator if available, falling back to the Python statevector simulator if not present.
More flexible names and parameters¶
Several functions of the SDK have been made more flexible and user-friendly:
automatic circuit and register names
qiskit.ClassicalRegister,qiskit.QuantumRegisterandqiskit.QuantumCircuitcan now be instantiated without explicitly giving them a name - a new autonaming feature will automatically assign them an identifier:q = QuantumRegister(2)
Please note as well that the order of the parameters have been swapped
QuantumRegister(size, name).methods accepting names or instances
In combination with the autonaming changes, several methods such as
qiskit.Result.get_data()now accept both names and instances for convenience. For example, when retrieving the results for a job that has a single circuit such as:qc = QuantumCircuit(..., name='my_circuit') job = execute(qc, ...) result = job.result()
The following calls are equivalent:
data = result.get_data('my_circuit') data = result.get_data(qc) data = result.get_data()